Water resource predictive analysis method based on artificial intelligence

Abstract

The invention relates to the technical field of water resource analysis, and discloses a water resource predictive analysis method based on artificial intelligence. The method relates to the technical field of water resource analysis, and comprises the following steps: acquiring an original hydrological data set including rainfall intensity, river flow and the like through a sensing terminal, and performing multi-modal data alignment to generate a hydrological space-time tensor; constructing a dynamic water level threshold response mechanism in combination with watershed topographic features to obtain a partition water level calibration matrix; inputting the hydrological feature map into a spatial-temporal feature coupling network containing a long-short-term memory module and a spatial self-attention module to generate a hydrological feature map; constructing a multi-dimensional abnormal association tensor based on the multi-dimensional abnormal association tensor, and identifying rainfall flood event nodes by using an adaptive sliding window detection algorithm; and an optimal hydrological parameter set is obtained through genetic algorithm optimization, and the three-dimensional hydrological dynamic model is driven to establish a mapping relation chain. The method can effectively fuse hydrological data spatio-temporal characteristics, and improves the accuracy and efficiency of water resource prediction analysis.

Description

Technical Field

[0001] This invention relates to the field of water resources analysis technology, specifically to a predictive analysis method for water resources based on artificial intelligence. Background Technology

[0002] The dynamic changes in water resources are closely related to the ecology and social development of watersheds. Real-time monitoring of hydrological conditions and forward-looking analysis are important directions for watershed management. Currently, hydrological monitoring has gradually introduced sensing equipment to collect parameters such as precipitation intensity, river flow, and groundwater level, but the existing system still has significant limitations.

[0003] In most river basins, sensor terminals are dispersed, and the raw data collected often exhibits multimodal characteristics. Timestamps for data such as precipitation and flow are frequently misaligned due to device clock deviations and transmission delays, making direct correlation analysis difficult. During data processing, traditional methods often employ single-dimensional statistical analysis, focusing solely on time-series changes or isolating spatial distribution differences, neglecting the inherent relationship between temporal evolution and spatial diffusion in hydrological phenomena. This hinders the full uncovering of the underlying hydrological patterns within the data.

[0004] The setting of water level thresholds directly affects the accuracy of hydrological status assessment. Existing methods often use a uniform fixed threshold, without considering the differences in topographic features of different watersheds. The water level carrying capacity and confluence velocity of mountainous watersheds and plain watersheds are significantly different. Fixed thresholds can easily lead to misjudgment in high-altitude areas or omission in low-lying areas.

[0005] In stormwater event detection, traditional sliding window algorithms often use preset window sizes, which cannot adaptively adjust to dynamic changes in precipitation intensity and river flow. When hydrological parameters change abruptly, detection lag or false alarms are likely to occur. Furthermore, the determination of hydrological model parameters often relies on empirical values ​​or single-objective optimization, making it difficult to take into account the synergistic relationships among multiple hydrological elements within the watershed. This results in limited matching between model output and actual hydrological entities.

[0006] The combination of these problems makes it difficult for existing water resource predictive analysis methods to form a coherent and accurate analysis chain when dealing with complex hydrological scenarios, and thus fails to fully meet the needs of watershed management for dynamic and comprehensive hydrological information. Summary of the Invention

[0007] The purpose of this invention is to provide an artificial intelligence-based predictive analysis method for water resources to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a water resource predictive analysis method based on artificial intelligence, the method comprising:

[0009] Raw hydrological datasets are collected in real time by sensor terminals deployed in multiple river basins. The raw hydrological datasets include at least time-series monitoring values ​​of precipitation intensity, river flow, groundwater level, and water quality parameters.

[0010] Perform multimodal data alignment on the original hydrological dataset to generate a hydrological spatiotemporal tensor with a unified timestamp.

[0011] A dynamic water level threshold response mechanism is established, and the hydrological spatiotemporal tensor is spatially partitioned and calibrated according to the watershed topographic features, and the partitioned water level calibration matrix is ​​output.

[0012] The partitioned water level calibration matrix is ​​input into a spatiotemporal feature coupling network, which includes a cascaded long short-term memory module and a spatial self-attention module to generate a hydrological feature map that fuses spatiotemporal features.

[0013] Based on the hydrological feature map, a multidimensional anomaly correlation tensor is constructed, and an adaptive sliding window detection algorithm is used to identify rainwater and flood event nodes.

[0014] A genetic algorithm is used to perform multi-objective parameter optimization iteration on the hydrological feature map to generate the optimal set of hydrological parameters.

[0015] The optimal set of hydrological parameters drives the three-dimensional hydrological dynamic model, establishing a mapping relationship chain between model parameters and hydrological entities.

[0016] Preferably, performing multimodal data alignment on the original hydrological dataset includes:

[0017] The precipitation pulse sequence, river flow waveform, groundwater level depth curve, and water quality spectral characteristics are separated from the raw data stream acquired by the sensing terminal.

[0018] Abnormal offset values ​​in each data stream are detected, and missing data segments are filled using spatiotemporal kriging interpolation based on adjacent sensor nodes; frequency domain transformation is performed on the filled data stream to decompose the time domain waveform into frequency domain feature vectors; the frequency domain feature vectors are normalized and compressed to form a standardized frequency domain matrix.

[0019] Periodic features and spatial correlation features are extracted from the standardized frequency domain matrix; the periodic features, spatial correlation features and the standardized frequency domain matrix are combined into a hydrological spatiotemporal tensor with a time dimension identifier.

[0020] Preferably, the establishment of a dynamic water level threshold response mechanism includes:

[0021] Hydrological response units are divided based on the watershed digital elevation model, and dynamic water level response thresholds are set for each unit.

[0022] The dynamic water level response threshold is corrected based on the historical flood event database, and a regional water level threshold comparison table is generated.

[0023] The hydrological spatiotemporal tensor is spatially partitioned according to hydrological response units to form multiple hydrological unit sub-tensors;

[0024] The water level threshold comparison table for each zone is used to calibrate the water level reference for each hydrological unit sub-tensor; the calibration offset of each hydrological unit sub-tensor is calculated using a Gaussian process regression model.

[0025] The calibration offset is applied to dynamically correct the hydrological unit sub-tensor; all corrected hydrological unit sub-tensors are merged to generate a partitioned water level calibration matrix.

[0026] Preferably, the operation of the spatiotemporal feature coupling network includes:

[0027] A cascaded feature extraction architecture is constructed. The front-end uses a bidirectional gated loop unit to capture the flood peak propagation time-series features in the partitioned water level calibration matrix. The back-end uses a spatial self-attention mechanism to analyze the topological association features between watershed nodes. Feature weighted fusion is performed on the flood peak propagation time-series features and topological association features to generate a hydrological feature map with spatial dependency labels. The confidence of the hydrological feature map is verified by a feature importance evaluation module.

[0028] Preferably, constructing a multidimensional anomaly correlation tensor based on the hydrological feature map includes:

[0029] Separate the precipitation intensity feature layer, river flow feature layer, and water quality change feature layer from the hydrological feature map; establish a cross-feature layer correlation matrix to identify the abnormal transmission paths between different feature layers; use an adaptive sliding window detection algorithm to scan the abnormal patterns in the correlation matrix; identify the rainstorm event triggering nodes and pollution diffusion nodes in the abnormal patterns; and output a set of abnormal event coordinates containing spatial location markers.

[0030] Preferably, the multi-objective parameter optimization iteration using a genetic algorithm includes:

[0031] Define an optimization objective function that includes water supply security rate, flood control rate, and ecological water demand satisfaction rate; initialize a population of hydrological parameters including river roughness coefficient, infiltration rate parameter, and reservoir scheduling rules; select dominant parameter individuals through a tournament selection mechanism; perform multi-point crossover mutation operation on the dominant parameter individuals; update the parameter population using an intergenerational migration strategy; and output the optimal set of hydrological parameters that satisfies the Pareto front when the maximum number of generations is reached.

[0032] Preferably, the driving force for the three-dimensional hydrological dynamic model includes:

[0033] The optimal set of hydrological parameters is input into the hydrological model engine; a three-dimensional hydrological grid is constructed based on the watershed geographical boundary information; the river network topology data is mapped to the three-dimensional hydrological grid; a corresponding hydrological parameter instance is assigned to each grid node; the hydrological process line is simulated based on the parameter instantiation results; and a three-dimensional hydrological dynamic field with a time dimension is generated.

[0034] Preferably, the spatial self-attention module of the spatiotemporal feature coupling network performs the following operations:

[0035] A hydrological heterogeneous graph is constructed, where nodes represent monitoring stations and edges represent watershed connectivity. Spatial dependency weights between nodes are calculated, with the weights depending on the river channel distance and hydrological similarity. A multi-head attention mechanism is used to aggregate the feature representations of upstream nodes. The attention outputs of different heads are integrated through residual connections. Layer normalization is used to stabilize the feature distribution.

[0036] Preferably, the intergenerational transfer strategy of the genetic algorithm includes:

[0037] An elite parameter archive is established to store non-dominated solutions in each generation of evolution; when the population diversity is below a threshold, diverse individuals are injected from the elite parameter archive; Gaussian perturbation is applied to the injected individuals to expand the search space; the fitness values ​​of the perturbed individuals are recalculated; and the Pareto rank of the current parameter population is updated.

[0038] Preferably, the parameter instantiation process of the three-dimensional hydrological dynamic model includes:

[0039] The process involves analyzing the river hydraulic conduction coefficient from the optimal set of hydrological parameters; extracting the specific yield parameter of the groundwater aquifer; obtaining the reservoir scheduling rule function; mapping the hydraulic conduction coefficient to the river grid cell; assigning the specific yield parameter to the groundwater level simulation cell; embedding the scheduling rule function into the reservoir control node; verifying the completeness of the parameter mapping; and outputting an instantiation report.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] By deploying sensing terminals across multiple river basins to collect multi-dimensional raw hydrological data in real time, covering time-series monitoring values ​​of precipitation intensity, river flow, groundwater level, and water quality parameters, a comprehensive understanding of the hydrological state can be achieved, providing rich foundational data for subsequent analysis. Multimodal data alignment operations generate hydrological spatiotemporal tensors with unified timestamps, eliminating time-dimensional biases between different data types and establishing ordered correlations between various hydrological parameters along the time axis, making the data presentation more consistent with the natural evolution of hydrological phenomena.

[0042] The dynamic water level threshold response mechanism combines watershed topographic features to spatially partition and calibrate the hydrological spatiotemporal tensor. The output partitioned water level calibration matrix can adapt to the hydrological characteristics of different terrains, enabling water level assessments to fit the actual conditions of different watersheds such as mountains and plains, avoiding the problem of insufficient adaptability caused by a uniform threshold. The partitioned water level calibration matrix is ​​input into a spatiotemporal feature coupling network. This network captures the temporal evolution trend of hydrological parameters through cascaded long short-term memory modules, and simultaneously uses a spatial self-attention module to mine the spatial correlation of hydrological elements in different regions. The resulting hydrological feature map, which integrates spatiotemporal features, can more comprehensively reflect the intertwined relationship between time and space in hydrological phenomena.

[0043] A multidimensional anomaly correlation tensor is constructed based on hydrological feature maps. An adaptive sliding window detection algorithm is used to identify stormwater event nodes. This algorithm allows the window size to be flexibly adjusted according to the dynamic changes in hydrological parameters, enabling it to handle stormwater events of different intensities and types, thus enhancing the scenario adaptability of anomaly identification. A genetic algorithm is used to iteratively optimize multi-objective parameters on the hydrological feature maps. The resulting optimal set of hydrological parameters can take into account the synergistic effects of multiple hydrological elements within the watershed, avoiding parameter bias caused by single-objective optimization.

[0044] The three-dimensional hydrological dynamic model driven by the optimal set of hydrological parameters establishes a mapping relationship between model parameters and hydrological entities, which enables the model output to be closer to the actual state of hydrological entities. This allows the model to simulate hydrological processes more realistically and helps to deepen the understanding of dynamic changes in water resources. Attached Figure Description

[0045] Figure 1 This is a schematic diagram illustrating the working principle of the artificial intelligence-based water resource predictive analysis method described in this invention.

[0046] Figure 2 A flowchart for multimodal data alignment operations;

[0047] Figure 3 A flowchart of spatiotemporal feature coupling network operations;

[0048] Figure 4 This is a flowchart for a three-dimensional hydrological dynamic model. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1This invention provides a predictive analysis method for water resources based on artificial intelligence, the method comprising:

[0051] Raw hydrological datasets are collected in real time by sensor terminals deployed in multiple watersheds. These datasets include time-series monitoring values ​​of precipitation intensity, river flow, groundwater level, and water quality parameters. The sensor terminals include rain gauges, flow meters, water level gauges, and water quality monitors, continuously generating structured monitoring data at 5-minute sampling intervals.

[0052] A multimodal data alignment operation is performed on the original hydrological dataset to generate a hydrological spatiotemporal tensor with a unified timestamp. This operation includes four sub-steps: data cleaning, missing value imputation, time-frequency transformation, and feature fusion, ultimately forming a third-order tensor structure with dimensions of [time step × spatial nodes × feature channels].

[0053] A dynamic water level threshold response mechanism is established. Based on the watershed topographic features, the hydrological spatiotemporal tensor is spatially partitioned and calibrated, outputting a partitioned water level calibration matrix. A digital elevation model is used to divide the hydrological response units, and the water level benchmark value is calculated independently for each unit. Systematic errors are eliminated through Gaussian process regression.

[0054] The partitioned water level calibration matrix is ​​input into a spatiotemporal feature coupling network, which includes a cascaded long short-term memory module and a spatial self-attention module to generate a hydrological feature map that fuses spatiotemporal features. The long short-term memory module is configured with 64 hidden units to handle temporal dependencies, and the spatial self-attention module employs an 8-head attention mechanism to capture watershed topological relationships.

[0055] Based on the aforementioned hydrological feature maps, a multidimensional anomaly correlation tensor is constructed, and an adaptive sliding window detection algorithm is used to identify stormwater event nodes. The anomaly detection window length is dynamically adjusted according to the watershed response time, with a minimum detection granularity of 15 minutes.

[0056] A genetic algorithm was used to iteratively optimize the hydrological feature maps using multiple objectives, generating the optimal set of hydrological parameters. The algorithm population size was set to 200, the crossover probability to 0.85, the mutation probability to 0.15, and the upper limit of the number of generations to 500.

[0057] The optimal set of hydrological parameters drives a three-dimensional hydrological dynamic model, establishing a mapping relationship between model parameters and hydrological entities. The model uses the finite volume method to discretize the computational domain, with adaptive adjustment of the time step, and the smallest computational unit scale is 100 meters × 100 meters.

[0058] Example 1: See Figure 2The multimodal data alignment process coordinates heterogeneous data sources with second-level time precision. Sensor terminal deployment follows the principles of watershed hydrological node layout: rain gauges are deployed along rainfall gradient zones using the Thiessen polygon method; flow meters are installed at river control sections; water level gauges are deployed considering points of curvature change in the backwater curve; and water quality monitors cover pollution-sensitive areas. Raw data streams are transmitted to edge computing nodes via the LoRaWAN protocol, with transmission latency controlled within 800 milliseconds. Data separation is performed in the stream processing engine: precipitation pulse sequences record droplet impact events with millisecond timestamps and are accumulated to convert them into 5-minute cumulative rainfall values; river flow waveforms are obtained through raw sampling of ADCP acoustic Doppler profiles, extracting the cross-sectional average flow velocity; groundwater level depth curves are filtered to remove pressure fluctuation interference and smoothed using Kalman filtering for wellbore oscillations; water quality spectral characteristics are acquired using an ultraviolet spectrophotometer, and the raw spectral data undergoes second-order derivative processing to eliminate baseline drift.

[0059] Anomaly offset detection uses a dynamic standard deviation threshold, employing the moving interquartile range (MIRV) method during the dry season and a kurtosis correction factor during the flood season. When two consecutive sampling periods of missing data are detected, the system automatically retrieves the three nearest adjacent nodes based on their weights, prioritizing upstream stations according to river flow direction. The spatiotemporal kriging interpolation model incorporates a semivariance function adaptive selector, using a spherical model for groundwater level data and an exponential model for river flow. Spatial range parameters are adjusted based on the monthly hydrological index. The interpolation process incorporates a topographic transmission coefficient to calculate the effective distance weights within the line-of-sight. Three-dimensional point cloud reconstruction is performed on precipitation data, with radar echo-assisted correction.

[0060] Frequency domain transformation employs an overlapping segmented Fast Fourier Transform (FFT) strategy, with a 256-point FFT window sliding step size set to 50%, dividing the 5-minute time series data into 12 segments. Frequency domain feature vectors are truncated to the 0.01-0.5Hz band, covering the characteristic frequencies during the formation and recession phases of the rainstorm. Mel-scale filter banks are used for feature frequency band division, retaining 32 key frequency band energy values. The Hanning window width is set to an integer multiple of the sampling period (300 seconds), and the mirror continuation method is used to eliminate the Gibbs effect during boundary processing.

[0061] The normalization compression process is implemented in three stages: the first stage calculates the historical minimum and maximum values ​​for each frequency band independently for each monitoring station, with the reference period including data from the same period over the past three years; the second stage performs a linear transformation on the real-time values ​​to the [0,1] interval, and when a feature vector exceeding the historical extreme value is encountered, a sliding extreme value window is used to recalculate the benchmark; the third stage performs cosine similarity compression on the features between frequency bands, reducing the 32-dimensional features to 16 principal components, with the variance contribution rate threshold set to 95%. The construction of the normalized frequency domain matrix includes time axis alignment operations, generating a data slice containing all spatial points every 5 minutes, with stations within the slice sorted according to a watershed tree structure.

[0062] Periodic feature extraction employs a joint time-frequency analysis method: the diurnal variation period in the rainfall intensity sequence is identified through wavelet coherence spectrum, seasonal fluctuation components are separated using empirical mode decomposition, and tidal influence factors are detected by performing interharmonic analysis on groundwater level data. Feature identifiers include the principal period phase angle, amplitude stability index, and phase abrupt change markers. Spatial correlation feature calculation introduces a dynamic weighting system, adjusting the station correlation strength according to the real-time water flow direction: the correlation enhancement factor is 1.8 for downstream flow, 0.5 for upstream flow, and a neutral value of 1.0 for vertical flow. Feature dimensions include spatial cross-correlation lag time, peak covariance distance, and information entropy flow transmission rate.

[0063] The spatiotemporal tensor is constructed using a five-dimensional data cube structure: the time dimension is marked with Unix timestamps in 5-minute increments; the spatial dimension is divided into 100-meter grids according to UTM partitioning; the feature dimension includes 12 original monitoring channels, 16 compressed frequency domain features, 8 periodic feature indicators, and 6 sets of spatial correlation vectors. Time synchronization calibration is performed during data cube assembly, using the NTP protocol to align clock errors across terminals. The final tensor structure contains traceable data lineage metadata, recording the original source and processing path of each feature. Tensor memory allocation employs a block storage strategy, with hierarchical indexing implemented in the time dimension to accelerate time-series retrieval. The storage accuracy of this data structure maintains the physical resolution of the original data acquisition, and spatial coordinate errors are controlled within the accuracy range of the positioning equipment (GNSS positioning error < 3 meters).

[0064] Example 2: See Figure 3 The construction of the dynamic water level threshold response mechanism begins with the scientific division of hydrological response units. Based on the 1:10000 scale digital elevation model data of the watershed, an improved D8 flow direction algorithm is used for hydrological analysis to accurately identify natural confluence boundaries. Unit division prioritizes respecting geomorphic differentiation characteristics, including natural boundary elements such as ridgelines and valley lines, ensuring relatively homogeneous topographic conditions within each unit. The dynamic water level response threshold is set using a seasonal and scenario-based strategy. During the dry season, the statistical values ​​of the historical lowest water levels at hydrological stations over the past five years are referenced, and an upward buoyancy margin is introduced. This margin is dynamically adjusted based on the groundwater recharge rate calculation results. During the wet season, a rainfall-runoff relationship coefficient chain correction system is activated, which integrates the previous soil moisture saturation index and the real-time rainfall intensity weighting factor. The application of the historical flood event database is reflected in the analysis of inundation frequency distribution through kernel density estimation algorithms, thereby improving the water level threshold sensitivity of high-frequency inundation areas. The spatial partitioning process of the hydrological spatiotemporal tensor strictly follows the vector boundary of the hydrological response unit, and the hydrological unit sub-tensor formed after partitioning maintains the original time step and characteristic channel complete structure.

[0065] The application of the zoned water level threshold comparison table reflects the principle of differentiated calibration rules. Each hydrological unit sub-tensor is independently matched with a specific threshold standard, and the calibration benchmark value is determined comprehensively based on the main river morphology, riverbed sediment permeability characteristics, and local drainage capacity within the unit. The hyperparameter optimization of the Gaussian process regression model is achieved using Bayesian inference, and the initial value of the radial basis function covariance kernel's length scale is set with reference to the logarithmic value of the unit area. The calculation process of the calibration offset incorporates the spatial heterogeneity influence correction matrix, which quantifies the confluence deviation caused by uneven spatial distribution of rainfall, the change in infiltration rate caused by differences in soil type, and the surface roughness fluctuation caused by vegetation cover. The offset is expressed as a standardized value consistent with the scale of the original data, and the dynamic correction process uses matrix multiplication to complete the benchmark value transformation while maintaining the data distribution characteristics. All corrected hydrological unit sub-tensors are reconstructed through a spatial index stitching engine. The reconstructed zoned water level calibration matrix retains the calibration log of each unit, including a traceable data structure of original value, offset, and corrected value, and spatial continuity is maintained through a buffer-weighted smoothing algorithm.

[0066] The spatiotemporal feature coupling network is implemented using a cascaded architecture design, with the partitioned water level calibration matrix serving as the input tensor into the processing flow. The front-end bidirectional gated recurrent unit is configured as a two-layer stacked structure, with each layer containing 128 hidden units for temporal feature encoding. The state update process of the gated unit incorporates rainfall impact factors; the unit input gate receives a combination signal of the current calibrated water level value and rainfall intensity, while the forget gate design focuses on the difference between the flood front peak and the baseflow state. Temporal feature extraction focuses on identifying the flood peak propagation pattern, using a sliding time window to cumulatively analyze the three-dimensional parameter features of peak occurrence time, water level rise slope, and flood peak duration. The back-end spatial self-attention mechanism constructs the graph using a watershed node topological adjacency matrix. The inter-node dependency weights are calculated based on both river arrival time and elevation gradient, and a slope factor is introduced as an attention bias coefficient to enhance the mainstream path identification capability. Multi-head parallel computation is divided into 8 independent subspaces, with each head's feature projection dimension set to 64 dimensions. A proportional controller is applied to the residual connections to balance the original input and attention output.

[0067] Feature fusion employs spatially dependent label embedding technology, where temporal feature vectors and spatial feature tensors are weighted and concatenated along the feature dimension. The weighting coefficients are dynamically generated using a learnable parameter matrix, which receives the current spatial distribution characteristics of rainfall as contextual reference. The fusion process utilizes a three-dimensional tensor broadcasting mechanism to maintain the independence of feature vectors at each spatial node. The feature importance assessment module employs a saliency mapping analysis method to calculate the activation contribution of each feature channel to historical flood events. A scalar value between 0 and 1 is generated based on the feature confidence metric using information entropy. Feature channels below a dynamic threshold are masked; this threshold is automatically calibrated based on the inflection point of the classification accuracy curve on the validation dataset. The final output hydrological feature map exhibits clear spatiotemporal correlation characteristics. Its data structure is organized in tensor form: the first dimension corresponds to 60 consecutive steps in the time series (covering a five-hour range before and after the real-time monitoring point); the second dimension maps 1534 effective grid cells of the watershed spatial subdivision; and the third dimension is the integrated and fused high-order feature vector (a total of 48 effective feature channels). Regularization techniques are introduced into the network processing flow to control complexity, including random node masking with a drop rate of 0.2 and gradient pruning of L2 constraint coefficients. Computational resources are configured in a distributed multi-node GPU collaborative computing mode.

[0068] Example 3: This example encompasses two interconnected technical layers: multidimensional anomaly correlation tensor construction and genetic algorithm optimization. In the multidimensional analysis of hydrological feature maps, the precipitation intensity feature layer extracts the cumulative hourly rainfall distribution within a 3×3 neighborhood using convolutional kernels, separating the hourly rainfall intensity extremes, peak duration, and storm center movement trajectory indicators. The river flow feature layer employs directional gradient histogram analysis to quantify the flood wavefront propagation rate, single / multiple peak morphology characteristics, and the ternary values ​​of the water level rise rate. The water quality mutation feature layer focuses on the temporal evolution characteristics of pollution events, including the minute-level decline slope of dissolved oxygen saturation, the duration of sudden increases in chemical oxygen demand, and the diffusion wavefront migration speed of heavy metal pollutants.

[0069] The cross-feature layer correlation matrix construction performs tensor product operations to generate a fifth-order correlation structure with dimensions of [rainfall feature × flow feature × water quality feature × spatial coordinates × timestamp]. This structure embeds an anomaly transmission path identification algorithm, based on an improved random walk graph model: defining virtual transmission nodes whose influence scores increase exponentially with path connectivity strength; and combining the topological order of water flow direction and the half-life factor of pollutant diffusion with the transmission path weights. The calculation formula is as follows:

[0070]

[0071] Where Δ is the anomaly propagation damping coefficient, β represents the mutual information entropy between characteristic nodes, L is the spatial displacement distance (km) along the river channel direction, τ characterizes the pollutant decay time constant (hours), and α is the empirical topographic inhibition index. This model quantifies the transmission efficiency of characteristic anomalies in the watershed network.

[0072] The adaptive sliding window detection algorithm implements a dynamic window adjustment mechanism. The initial scanning window covers a continuous 6-hour period. The window size is adjusted based on the anomaly density value ρ (the proportion of anomaly grids per unit time) within the detection period: when ρ > 0.3, the window shrinks logarithmically, down to a minimum of 1-hour resolution; when ρ < 0.1, the window linearly expands to the upper limit of 24 hours. The sliding step size maintains a fixed ratio of 0.2 times the window size. Anomaly pattern recognition uses matched filtering technology. The identification of stormwater event trigger nodes must simultaneously meet three rigid criteria: the five-minute rainfall intensity exceeds the historical 95th percentile, the instantaneous growth rate of river flow exceeds the threshold of 20% / hour, and the anomaly synchronization rate of neighboring nodes with a spatial correlation degree higher than 0.8 exceeds 70%. The determination of pollution diffusion nodes introduces a migration consistency verification step, tracking the angle between the direction of the water quality anomaly wavefront peak motion vector and the hydrological flow velocity vector (threshold < 30°), and detecting the chain growth phenomenon of pollutant concentration gradient in the downstream of the basin.

[0073] The multi-objective optimization system of the genetic algorithm is designed with three objective functions: Water supply security objective quantifies the water shortage depth of water-using units on a monthly scale, and uses the Palmer drought index correction algorithm to calculate periodic water deficit; Flood control objective integrates the cumulative spatial area and duration of water levels exceeding warning levels to establish a three-dimensional integral surface for inundation risk; Ecological water demand objective sets a flow pulse frequency index for fish migration periods and extracts key fluctuation characteristics of the river flow process curve. Stratified sampling strategy is used for parameter population initialization: the river roughness coefficient is stratified into intervals of [0.025, 0.035], [0.035, 0.045], and [0.045, 0.065], the infiltration rate parameter is divided into eight orders of magnitude with exponential intervals, and the reservoir scheduling rules are encoded as an operational protocol chain containing 20-dimensional decision variables such as the pre-storage water level, maximum discharge, and flood control limit. The population size is set to 200 parameter combinations, and chromosome encoding uses real-valued encoding.

[0074] In the evolutionary process, the tournament selection uses a five-round elimination system, randomly selecting five individuals in each round for fitness competition. Fitness evaluation employs a non-dominated ranking mechanism. First, normalized loss matrices for the three objective functions are generated, followed by the construction of a dominance graph among individuals. For dominant parameters, individuals with higher Pareto rank are prioritized; when ranks are the same, individuals with greater crowding distance are selected. The multi-point crossover operation randomly generates three cutoff points on the chromosome to perform gene recombination. The interval between cutoff points is constrained by physical constraints between parameters (e.g., the roughness coefficient must be less than the critical value for riverbank erosion). The mutation operator uses a polynomial perturbation mechanism, applying offsets to 15% of gene loci, with the mutation length adaptively adjusted based on parameter sensitivity coefficients.

[0075] An intergenerational migration strategy constructs an elite parameter archive, archiving non-dominated solutions after each generation of evolution. When the population's genetic diversity drops to a threshold (average Hamming distance < 0.3), the 10 solutions with the furthest crowding distance are extracted from the archive and injected into the population. Gaussian white noise perturbation is applied to the injected individuals, with the noise intensity set differently based on parameter type: the standard deviation of continuous parameters is 1 / 20 of the feasible region, and a bit-flipping operation is performed on discrete parameters. After perturbation, the fitness value of the individuals needs to be re-evaluated. The population Pareto rank refresh mechanism adopts an incremental update method, calculating dominance relationships only for newly added solutions and affected individuals. The final output optimal parameter set must satisfy the Pareto front convergence condition: the overlap of non-dominated solution sets exceeds 95% for ten consecutive generations, and the projection of the objective function value into the parameter hyperspace forms a stable envelope. This set is automatically converted into a model-driven configuration file in XML format, containing three parts of structured data: river hydraulic parameter segments, groundwater conversion coefficient matrix, and reservoir scheduling rule state transition table. Genotype similarity is defined as not exceeding 70%. Pareto front screening uses crowding distance sorting and ultimately outputs a set of optimization parameters containing 50 non-dominated solutions.

[0076] Example 4: See Figure 4 The driving process of the three-dimensional hydrological dynamic model is implemented in a tributary basin in the middle reaches of the Yangtze River. During the model initialization phase, 1:50,000 digital elevation data is loaded, and an unstructured mesh is generated using the constrained Delaunay triangulation method. A base resolution of 100 meters is maintained in the plain area, while the main river channel and confluences are densified to a 50-meter mesh. In steep slope areas, the mesh is adaptively densified according to the terrain curvature, reaching a maximum density of 20 meters. River network topology data is imported from the river system shapefile, extracting 2578 river segment nodes and constructing an adjacency matrix to store upstream and downstream relationships, gradient values, and 124 measured cross-sectional morphological parameters. The basin boundary is divided into 32 hydrological calculation zones, each assigned an independent water balance verification module.

[0077] Structured parsing is performed when loading the optimal set of hydrological parameters: three types of parameters, namely the river channel hydraulic conductivity, the groundwater aquifer specific yield, and the reservoir operation rules, are separated from the XML configuration file output by the genetic algorithm. The river channel hydraulic parameters include 217 numerical values such as the main channel roughness coefficient of 0.032, the floodplain coefficient of 0.067, and the local bend correction factor of 1.15. These are mapped to the grid edge cells according to the river section ID mapping table. The conversion process of the permeability tensor takes into account the bed material composition (65% gravel layer and 35% clay layer). The groundwater aquifer parameters are assigned values layer by layer based on the geological exploration profile: the specific yield of the shallow unconfined aquifer is 0.21, the storage rate of the middle confined aquifer is 0.0007, and the elastic specific yield of the deep bedrock fissure water is 0.0043. The finally generated layered parameter matrix covers the entire groundwater simulation area.

[0078] The dynamic compilation process is implemented for the reservoir operation rules. The Xiaolangdi Reservoir control node receives the flood control limit line operation rules: when the inflow discharge Q ≤ 3000 m 3 / s, it operates according to the water supply mode; when 3000 < Q ≤ 5000, flood pre-discharge is started; when Q > 5000, the full-load flood discharge plan is enabled. This rule is compiled into a state transition query table, which is associated with the water level - storage curve and the NQH characteristic surface of 8 units. When the parameter instantiation verification module detects the lack of permeability parameters in the grid area of the dam site, it automatically calls the geological analogy database and completes the parameter values by referring to similar engineering cases. After complete instantiation, a parameter mapping report is generated, indicating that 98.7% of the grids have been effectively assigned values, and the remaining 1.3% of the corner areas use adjacent interpolation.

[0079] The operation mechanism of the spatial self-attention module in hydrological feature parsing is as follows: A heterogeneous graph covering 562 monitoring stations in the whole basin is constructed. The node attributes include a 12-dimensional feature vector such as station elevation, catchment area, and river network density. The definition of the edges follows the principle of water system connectivity: bidirectional connections are set in the mainstream direction, and directed edges are created at the confluence points of tributaries. The calculation of the spatial dependence weight adopts a two-factor model: the river flow reach distance is calculated by the Dijkstra algorithm to obtain the water system path length, and the hydrological similarity comprehensively considers the annual average flow variation coefficient and the flood response slope of the stations.

[0080] This module adopts an 8-head parallel attention architecture. When processing the Wuhan Hydrological Station node, the query vector focuses on its flood peak formation characteristics. In the calculation of the key vector, the flow conduction characteristics of the upstream Yichang Station are preferentially associated, and the value vector integrates the influence of the water level fluctuation downstream of the Three Gorges Dam. The attention weight of the Hanjiang River inlet node obtains a high score of 0.85, reflecting the significance of the cross-water system interaction. The residual connection mechanism retains 40% of the weight of the original elevation characteristics of the stations, avoiding over-reliance on neighborhood information. The final output layer is processed by batch normalization, and the node feature vector is extended to 512 dimensions, of which 153 dimensions are specifically encoded for the basin topology association characteristics.

[0081] The execution flow of the 3D dynamic simulation adopts a step-by-step coupling strategy. The surface water module is solved based on the 2D shallow water equation, with an initial time step of 60 seconds, which is automatically compressed to 15 seconds when the flood wave velocity exceeds 2 m / s. The groundwater module uses a variable saturation zone model, updating the water table boundary conditions hourly. Data exchange is implemented between the two at the river channel seepage interface: water flux transfer is performed hourly at the grid edge nodes, and the exchange volume is automatically corrected based on the permeability coefficient of the riverbed sediments.

[0082] A real-time verification mechanism is activated during the simulation: 24 virtual flowmeters are set up in the Jingjiang flood diversion area. When the deviation between the calculated flow rate and the measured value exceeds 5% for three consecutive steps, an online parameter fine-tuning program is triggered. The model output includes an hourly updated 3D hydrodynamic field distribution map, with 16 stratigraphic slices in the depth direction to visualize the aquifer dynamics; a flooding vector layer is generated synchronously during the flooding process, with a water depth resolution of 0.1 meters; the water quality module tracks the diffusion trajectory of ammonia nitrogen pollution plumes and marks the locations of water intakes that may exceed standards within 12 hours. All output data is encapsulated into a spatiotemporal data cube structure, accompanied by complete metadata descriptors and coordinate reference system information.

[0083] Example 5: Focusing on the collaborative operation mechanism of genetic algorithm population optimization strategy and 3D model parameter instantiation. In the genetic algorithm generational migration strategy, the elite parameter archive adopts a circular buffer storage structure, with a maximum capacity set to one-fifth of the number of individuals in each generation. Archive updates implement strict frontier non-dominated solution screening: after each evolutionary generation, the Pareto ranking of the non-dominated individuals in the current population is compared with the existing solutions in the archive, retaining only the solutions at the highest level in the dominance graph. When the total archive exceeds the capacity limit, a pruning mechanism based on crowding distance is initiated, deleting individuals from densely distributed regions in the target space. Population diversity monitoring is achieved by calculating the average Hamming distance in the genotype space, with a threshold set at the percentage of 0.3 differentiating sites.

[0084] When population diversity is detected to be below a set threshold, a diversity injection procedure is initiated. Individuals with the maximum crowding distance are selected from the elite archive as seeds, with the injection quantity controlled between 8% and 10% of the current population size. Before injection, targeted perturbation is implemented: continuous parameters are subjected to finite perturbations based on their range, with the perturbation intensity set according to parameter sensitivity levels—high-sensitivity parameters have perturbation amplitudes controlled within 0.5%, medium-sensitivity parameters are allowed a shift of 1% to 3%, and low-sensitivity parameters have a maximum perturbation of no more than 5%; discrete parameters undergo a flip mutation operation, randomly adjusting integer encoding values ​​under reasonable constraints. After perturbation, the fitness values ​​of the three objective functions are re-evaluated, and the evaluation results are incorporated into the current population's non-dominated ranking system.

[0085] Pareto rank reconstruction employs an incremental update algorithm, re-determining dominance relationships only for newly injected individuals and their directly affected solutions. The rank partitioning process constructs a dynamic hierarchical structure: the top level stores the set of solutions currently not dominated by any individual; the second level stores the set of individuals dominated only by the top level; and so on, forming a multi-level rank sequence. The crowding distance calculation for each level of individuals is performed within a multi-objective hyperspace, with the distance value accumulated based on the coordinate differences between adjacent solutions across various object dimensions. This reconstruction mechanism avoids the computational overhead of reordering the entire population.

[0086] The complete process of instantiating parameters for a 3D hydrological dynamic model includes a structured data transformation process. In the hydraulic conduction coefficient analysis stage, the anisotropic characteristics of the river network are addressed: the main conduction direction is consistent with the channel orientation; the longitudinal and transverse conduction ratios are automatically calibrated based on the riverbed sediment structure, assigning a value of 10:1 for sandy riverbeds, 7:1 for pebble riverbeds, and 15:1 for bedrock channels. Groundwater aquifer parameters are assigned vertically in layers: a dynamic storage model is used for shallow unconfined aquifers, with their specific yield parameters dynamically adjusted based on the vadose zone saturation curve; a fixed elastic storage coefficient is applied to intermediate confined aquifers; and a low permeability threshold is set for deep bedrock fissure water systems.

[0087] The reservoir scheduling rule function parsing adopts a state machine transition mode. The scheduling rules are compiled into five-dimensional decision variables: flood control limit water level, beneficial water level fluctuation range, dead water level warning line, maximum discharge capacity coefficient, and gradient discharge control parameters. The discharge curve is discretized into fifty control nodes, each associated with a water level-discharge relationship pair. During function instantiation, rule conflicts are checked, such as whether the priority settings for flood control and power generation needs form a closed-loop logic. Parameter mapping implements spatial topology correction: river hydraulic parameters are assigned to two-dimensional grid edge cells, and the grid automatically associates with the feature parameters of the nearest river segment; groundwater parameters are assigned to three-dimensional volume grid cells; and reservoir control parameters are embedded into the corresponding dam structure control node groups.

[0088] The parameter mapping integrity verification module implements a triple verification mechanism. The first layer verifies grid cell coverage, ensuring all valid computational cells receive parameter assignments. The second layer verifies physical constraints, checking whether the channel roughness exceeds the material property range and whether the infiltration rate matches the soil type. The third layer performs pre-verification of the mass balance: under zero-step conditions, a standard test flood hydrograph is input to calculate the water balance error coefficient for the entire basin, with an upper limit of tolerance set at 0.05%. The instantiation report generation uses a tree structure: the root node indicates the parameter set version; branch levels record the mapping status of each subsystem; leaf nodes contain detailed parameter sources, conversion factors, and value range status for each grid cell. The report simultaneously outputs a parameter sensitivity spectrum diagram, identifying the ten most influential core parameters. The entire process establishes a traceable data link, forming a closed-loop conversion chain from optimization output to simulation input.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A water resource predictive analysis method based on artificial intelligence, characterized in that, Includes the following steps: Raw hydrological datasets are collected in real time by sensor terminals deployed in multiple river basins. The raw hydrological datasets include at least time-series monitoring values ​​of precipitation intensity, river flow, groundwater level, and water quality parameters. Perform multimodal data alignment on the original hydrological dataset to generate a hydrological spatiotemporal tensor with a unified timestamp. A dynamic water level threshold response mechanism is established, and the hydrological spatiotemporal tensor is spatially partitioned and calibrated according to the watershed topographic features, and the partitioned water level calibration matrix is ​​output. The partitioned water level calibration matrix is ​​input into a spatiotemporal feature coupling network, which includes a cascaded long short-term memory module and a spatial self-attention module to generate a hydrological feature map that fuses spatiotemporal features. Based on the hydrological feature map, a multidimensional anomaly correlation tensor is constructed, and an adaptive sliding window detection algorithm is used to identify rainwater and flood event nodes. A genetic algorithm is used to perform multi-objective parameter optimization iteration on the hydrological feature map to generate the optimal set of hydrological parameters. The optimal set of hydrological parameters drives the three-dimensional hydrological dynamic model, establishing a mapping relationship chain between model parameters and hydrological entities.

2. The water resource predictive analysis method based on artificial intelligence according to claim 1, characterized in that, Performing multimodal data alignment on the original hydrological dataset includes: The precipitation pulse sequence, river flow waveform, groundwater level depth curve, and water quality spectral characteristics are separated from the raw data stream acquired by the sensing terminal. Abnormal offset values ​​in each data stream are detected, and missing data segments are filled using spatiotemporal kriging interpolation based on adjacent sensor nodes; frequency domain transformation is performed on the filled data stream to decompose the time domain waveform into frequency domain feature vectors; the frequency domain feature vectors are normalized and compressed to form a standardized frequency domain matrix. Periodic features and spatial correlation features are extracted from the standardized frequency domain matrix; the periodic features, spatial correlation features and the standardized frequency domain matrix are combined into a hydrological spatiotemporal tensor with a time dimension identifier.

3. The water resource predictive analysis method based on artificial intelligence according to claim 2, characterized in that, Establishing a dynamic water level threshold response mechanism includes: Hydrological response units are divided based on the watershed digital elevation model, and dynamic water level response thresholds are set for each unit. The dynamic water level response threshold is corrected based on the historical flood event database, and a regional water level threshold comparison table is generated. The hydrological spatiotemporal tensor is spatially partitioned according to hydrological response units to form multiple hydrological unit sub-tensors; The water level threshold comparison table for each zone is used to calibrate the water level reference for each hydrological unit sub-tensor; the calibration offset of each hydrological unit sub-tensor is calculated using a Gaussian process regression model. The calibration offset is applied to dynamically correct the hydrological unit sub-tensor; all corrected hydrological unit sub-tensors are merged to generate a partitioned water level calibration matrix.

4. The water resource predictive analysis method based on artificial intelligence according to claim 3, characterized in that, The operation of the spatiotemporal feature coupling network includes: A cascaded feature extraction architecture is constructed. The front-end uses a bidirectional gated loop unit to capture the flood peak propagation time-series features in the partitioned water level calibration matrix. The back-end uses a spatial self-attention mechanism to analyze the topological association features between watershed nodes. Feature weighted fusion is performed on the flood peak propagation time-series features and topological association features to generate a hydrological feature map with spatial dependency labels. The confidence of the hydrological feature map is verified by a feature importance evaluation module.

5. The artificial intelligence-based water resource predictive analysis method according to claim 4, characterized in that, Constructing a multidimensional anomaly correlation tensor based on the aforementioned hydrological feature map includes: Separate the precipitation intensity feature layer, river flow feature layer, and water quality change feature layer from the hydrological feature map; establish a cross-feature layer correlation matrix to identify the abnormal transmission paths between different feature layers; use an adaptive sliding window detection algorithm to scan the abnormal patterns in the correlation matrix; identify the rainstorm event triggering nodes and pollution diffusion nodes in the abnormal patterns; and output a set of abnormal event coordinates containing spatial location markers.

6. The artificial intelligence-based water resource predictive analysis method according to claim 5, characterized in that, Iterative optimization of multi-objective parameters using genetic algorithms includes: Define an optimization objective function that includes water supply security rate, flood control rate, and ecological water demand satisfaction rate; initialize a population of hydrological parameters including river roughness coefficient, infiltration rate parameter, and reservoir scheduling rules; select dominant parameter individuals through a tournament selection mechanism; perform multi-point crossover mutation operation on the dominant parameter individuals; update the parameter population using an intergenerational migration strategy; and output the optimal set of hydrological parameters that satisfies the Pareto front when the maximum number of generations is reached.

7. The water resource predictive analysis method based on artificial intelligence according to claim 6, characterized in that, The driving force for the three-dimensional hydrological dynamic model includes: The optimal set of hydrological parameters is input into the hydrological model engine; a three-dimensional hydrological grid is constructed based on the watershed geographical boundary information; the river network topology data is mapped to the three-dimensional hydrological grid; a corresponding hydrological parameter instance is assigned to each grid node; the hydrological process line is simulated based on the parameter instantiation results; and a three-dimensional hydrological dynamic field with a time dimension is generated.

8. The water resource predictive analysis method based on artificial intelligence according to claim 7, characterized in that, The spatial self-attention module of the spatiotemporal feature coupling network performs the following operations: A hydrological heterogeneous graph is constructed, where nodes represent monitoring stations and edges represent watershed connectivity. Spatial dependency weights between nodes are calculated, with the weights depending on the river channel distance and hydrological similarity. A multi-head attention mechanism is used to aggregate the feature representations of upstream nodes. The attention outputs of different heads are integrated through residual connections. Use layer normalization to stabilize the feature distribution.

9. The water resource predictive analysis method based on artificial intelligence according to claim 8, characterized in that, The generational transfer strategies of genetic algorithms include: An elite parameter archive is established to store non-dominated solutions in each generation of evolution; when the population diversity is below a threshold, diverse individuals are injected from the elite parameter archive; Gaussian perturbation is applied to the injected individuals to expand the search space; the fitness values ​​of the perturbed individuals are recalculated; and the Pareto rank of the current parameter population is updated.

10. The water resource predictive analysis method based on artificial intelligence according to claim 9, characterized in that, The parameter instantiation process of a three-dimensional hydrological dynamic model includes: The process involves analyzing the river hydraulic conduction coefficient from the optimal set of hydrological parameters; extracting the specific yield parameter of the groundwater aquifer; obtaining the reservoir scheduling rule function; mapping the hydraulic conduction coefficient to the river grid cell; assigning the specific yield parameter to the groundwater level simulation cell; embedding the scheduling rule function into the reservoir control node; verifying the completeness of the parameter mapping; and outputting an instantiation report.

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