86 results about "Flood forecasting" patented technology

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.

The invention provides a multi-mode coupling flood forecasting method and system based on a gated cross attention mechanism, and relates to the technical field of flood forecasting. The method comprises the following steps: acquiring high-precision rainfall monitoring information, numerical forecast rainfall information and hydrological station session flood excerpt information of a predetermined drainage basin; constructing training, verification and test samples; a spatial-temporal feature coding module based on a convolutional neural network and Transform is constructed, and rainfall information is fully mined; a time sequence feature coding module based on a time sequence convolutional network is constructed, and runoff information is fully mined; constructing a multi-modal feature fusion module based on a gated cross attention mechanism, and fusing different modal features mined by the time sequence and spatial-temporal feature coding modules; and constructing a flood prediction module based on a self-attention mechanism to perform flood prediction. According to the invention, the accuracy and reliability of flood forecasting are improved based on the multi-modal feature fusion technology.

The invention provides a watershed flood early warning system and method based on hydrological model and mobile application coupling, and belongs to the field of watershed flood early warning. In the system, a communication and data transmission module collects data for hydrological model simulation in a watershed area; the model simulation module simulates hydrological data of rainfall runoff in a watershed area in real time by using a hydrological model based on the collected data, and transmits a simulation result to the flood risk forecasting module through the communication and data transmission module; the flood risk forecasting module generates a flood risk map based on the received simulation result and transmits the flood risk map to the communication and data transmission module; and the mobile application display module receives real-time early warning information generated by the communication and data transmission module based on the flood risk map, and displays the real-time early warning information to a user. The watershed flood is simulated in real time through the hydrological model, the flood forecasting and early warning information is sent to people in a watershed flood submerging range through the mobile application, and the accuracy and timeliness of watershed flood early warning can be improved.

The invention discloses a flood forecast evaluation method adapted to reservoir dispatching characteristics, which comprises the following steps: respectively driving the same hydrological model by using historical observation rainfall and forecast rainfall, quantifying the error contribution of the hydrological model and rainfall forecast through double-track difference, and constructing a sample evaluation weight in combination with a rainfall score; constructing a weighting condition error distribution model based on the weight, and generating a reservoir flow forecasting scene set containing physical cause characteristics; and inputting the scene set into a reservoir scheduling model containing flood control and benefit-making rules, and outputting a comprehensive scheduling risk index containing a downstream over-alarm risk and water abandoning loss through water balance and downstream evolution calculation. According to the method, the statistical weight is corrected through error attribution, the abstract forecast error is converted into specific engineering risk and economic loss, the hidden risk working condition which is difficult to find by a traditional statistical index is effectively identified, and a quantitative basis is provided for risk decision making of reservoir dispatching and targeted optimization of a forecast system.

The invention discloses a large-scale flood risk dynamic assessment method for a power grid equipment flood-related area, and aims to solve the problems of risk assessment result distortion under complex hydrology, insufficient pertinence of power grid equipment flood risk assessment, insufficient risk assessment dynamic updating capability and the like in a traditional flood risk assessment method. According to the method, basic data such as meteorology, hydrology, terrain, remote sensing images and power grid historical flood conditions are integrated, and flood-prone areas are analyzed and determined; the method comprises the following steps: aiming at a flood-related area of power grid equipment, constructing a district distributed flood forecasting model based on a three-water-source Xinanjiang model, taking a hydrological forecasting model result as input, constructing a refined one-two-dimensional coupling flood routing analysis model, and simulating a flood inundation risk evolution process of the power grid equipment in different scenes; and constructing a power grid equipment multi-index flood inundation risk grading system to clarify the risk grade, constructing a large-scale flood risk assessment model, and outputting a dynamic risk assessment result by associating real-time data.

The invention discloses a reservoir flood forecasting method based on physical constraint and space-time double-flow coupling. The reservoir flood forecasting method comprises the following steps: S1, acquiring multi-source hydrometeorological data; s2, decomposing the multi-source hydro meteorological data into a historical state sequence and a future driving sequence; s3, performing feature extraction on the historical state sequence data through the physical enhanced long-short term memory network of the historical inertial feature extraction branch to obtain historical inertial features, and performing feature extraction on a future driving sequence through the time domain convolutional network of the future forced feature extraction branch to obtain future forced features; s4, performing weighted fusion on the historical inertial features and the future forced features to generate fusion features; and S5, inputting the fused features into a decoder to obtain a predicted water level increment, and superposing the predicted water level increment to the current water level to obtain a predicted value. The prediction timeliness is improved, the prediction precision of the water recession stage is improved, and the physical consistency of the prediction result is enhanced.

The invention discloses a river and lake regulation and storage system flood routing intelligent forecasting method based on a dynamic storage and discharge relation, and the method comprises the steps: constructing a space-time dynamic association diagram structure which reflects the waterpower relation of a drainage basin, and mapping multi-source hydrological data to generate a model input set with a unified space-time scale; establishing an aggregation reservoir parameterized structure model which comprises a dynamic water level-reservoir capacity relation driven by inflow change characteristics and a composite water level-flow relation integrating a time lag effect and backwater jacking feedback; constructing a flood regulation calculation equation set, and according to the real-time delay state in the composite relationship, adopting an adaptive solution mechanism to switch among different algorithm channels so as to solve the equation set; and correcting the model state by using the real-time observation data and executing rolling prediction. The method effectively solves the problems that in a complex river and lake system, dynamic characteristics of the storage and discharge relation are difficult to depict, the non-in-phase evolution process is unstable to solve, and high-dimensional parameters are difficult to calibrate, and the continuity and physical consistency of flood forecasting are improved.

The invention relates to the technical field of hydrology and water conservancy, and particularly discloses an intelligent real-time watershed flood early warning method and system, and the method comprises the steps: watershed data acquisition, fusion forecasting model construction, multi-stage early warning threshold calculation, early warning generation and online evaluation. According to the scheme, the real-time residual error of the physical hydrological model is constructed, the complex mode that the residual error changes along with the real-time rainfall intensity and the drainage basin state is learned by adopting the residual error prediction model based on machine learning, and an interpretable and steady forecast baseline and high-precision error correction are provided through a forward coupling and feedback coupling mixed architecture; the overall robustness of flood forecasting is effectively improved; dynamic inversion is carried out by using a fusion forecasting model, flood risk differences under different soil conditions are identified, the matching degree of early warning signals and actual risks is greatly improved, a comprehensive risk value is calculated in real time on a unified grid division system, and a risk zoning map is refreshed in real time along with updating of rainfall forecasting, so that the real-time prediction of the flood risk is realized. And the practicability of early warning information and the pertinence of emergency response are effectively improved.

The invention discloses a multi-target flood forecasting method based on coupling of a Xinanjiang model and deep learning. The method comprises the steps of 1, extracting a target watershed digital water system and subunit boundaries, and calculating watershed area rainfall; 2, hydrological data of a target drainage basin are collected, a data set is constructed, and an hourly rainfall-evaporation sequence and a secondary flood data set of the target drainage basin are obtained; 3, calibrating parameters of the Xinanjiang three-water-source model by adopting an NSGA-II optimization algorithm, generating a Pareto optimal solution set, and selecting a robustness parameter combination; and 4, jointly inputting the physical state variable output by the optimized Xinanjiang model and the original hydrological and meteorological data into a PatchTST deep learning model, capturing the nonlinear relationship between the physical state and the flow through a Transform architecture, generating corrected flow, and finally outputting a high-precision forecasting result. The method has the characteristics of multi-target collaborative parameter optimization, consideration of precision and physical consistency, accurate conversion of drainage basin geographic information and end-to-end high-robustness design.

The invention provides a GNSS (Global Navigation Satellite System) water vapor chromatography and deep learning-based flood forecasting method. The method comprises the following steps: S1, collecting GNSS, meteorological, flood and geographic data; s2, the PWV is estimated through a PPP equation and a Saastamoinen model; s3, grid water vapor density is calculated based on the three-dimensional water vapor chromatography technology; s4, constructing a Transform model, and performing rainfall prediction by taking PWV, water vapor density and the like as input; and S5, constructing a CNN-LSTM model based on rainfall prediction data to carry out flood forecasting. According to the rainfall and flood forecasting method established by the invention, GNSS multi-source and multi-mode navigation positioning signals are effectively utilized, the rainfall and flood forecasting method is not influenced by cloud and rain, all-weather observation can be realized, the water vapor gathering and conveying process in a three-dimensional space before rainfall can be carefully captured, the rainfall inversion process is optimized, the flood forecasting precision is improved, and the rainfall and flood forecasting period is prolonged; and a new thought is provided for flood control early warning of the drainage basin.

The application discloses a self-adaptive flood forecasting method, a terminal and a storage medium. The method comprises the following steps: generating a high signal-to-noise ratio local hydrological real-time data stream through adaptive sampling and a double-channel differential quantization technology; receiving an upstream input data stream, performing flood evolution calculation based on a viscoelastic dynamics model and fusing a dynamically identified input delay to obtain an original prediction value; for model parameters, a confidence ellipse is constructed based on ridge regression, the residual of real-time data and the prediction value is compared through statistical projection, the measurement noise and the river physical drift are intelligently distinguished, and the parameters are updated smoothly; finally, the original prediction value is corrected by solving a convex optimization problem subject to safety constraints, and a physically credible final prediction value is output. The corresponding terminal comprises corresponding modules for realizing the above steps. The application realizes high-precision, self-adaptive and intrinsically safe flood forecasting.

The invention belongs to the technical field of hydrological models and flood forecasting, and discloses a time-varying outflow coefficient-based flood runoff production simulation method, which comprises the following steps of interflow existence judgment, hydrological communication mechanism modeling, time-varying outflow coefficient model construction and parameter calibration and verification. According to the method, the behavior of the interflow is accurately described, the inflection point of the flood recession section can be accurately simulated, the flood forecasting precision of the semi-arid and semi-humid region is remarkably improved, and reliable technical support is provided for flood control and disaster reduction.

The present application relates to the technical field of hydrological resources, and specifically includes an intelligent short-impending fusion flood forecasting method based on a rain measurement radar, which comprises: receiving rain measurement detection information, performing prediction effect feedback in combination with a short-impending flood monitoring task at an edge adaptive correction node, configuring flood identification features based on a hydrological fusion prediction model, determining a flood peak time combination and a flooding risk, and generating a graded short-impending flood warning signal. The technical problem of the initial configuration of the hydrological fusion prediction model not matching the rain measurement data characteristics and the prediction target, the prediction deviation being unable to be corrected in time, and the deviation accumulation affecting the prediction reliability is solved by using a general hydrological model deployment mode, the technical effect of adaptively deploying the hydrological fusion prediction model by binding the core detection information of the rain measurement radar and the flood prediction accuracy requirement, the cooperative mechanism of the edge adaptive correction node and the RS485 bus interface, effectively avoiding deviation accumulation, and improving the stability of runoff simulation is achieved.

The invention relates to the technical field of flood prediction, solves the problem in the prior art that calculation of the total flow of a downstream section deviates due to neglect of superposition influence of reservoir flood during evolution along a river channel, and particularly relates to a reservoir dam break flood prediction method. Comprising the following steps: obtaining an initial parameter set of dam body parameters, hydrological parameters, terrain parameters and breach characteristic parameters of the rigid dam, preprocessing the initial parameter set to obtain a standard parameter set, and calculating a breach development rate and a breach width ratio when the rigid dam is identified as an instantaneous dam break according to the standard parameter set, and the instantaneous dam break is divided into a plurality of dam break sub-types in combination with the residual dam height proportion. According to the method, accurate time sequence superposition of the dam break flood and the reservoir flood is achieved, the calculation accuracy of the total flow of the downstream section is improved, the calculation result of the total instantaneous flow and the maximum flow of the downstream target section at any moment better fits the actual incoming flow condition, and integrated accurate prediction of the dam break flood flow, the propagation time and the downstream water level is achieved.

The present application relates to the technical field of flood forecasting, and more particularly to a city flood rapid forecasting method based on multi-process generalization. The method comprises the following steps: dividing sub-catchment by obtaining basic geographic information such as river system, terrain and rainwater pipe network of the target area, analyzing city underlying surface characteristics to calculate runoff, and calculating surface and pipe network confluence process based on improved equiflux line method and generalized trunk pipe model; through the interaction of trunk pipe and series-parallel virtual reservoir model, the surface detention, overland flow and return pipe are realized; finally, the one-dimensional river confluence and the cellular automata two-dimensional surface inundation model are coupled to realize the rapid simulation and visualization forecasting of city flood process. The present application efficiently couples runoff, surface confluence, pipe network flow limitation, surface and pipe network interaction, one-dimensional river confluence and two-dimensional inundation process through multi-process generalization, greatly reduces the calculation complexity and improves the timeliness of city flood forecasting.

The application discloses a large-scale basin deep learning flood forecasting method based on runoff lag information and relates to the technical field of hydrological simulation and flood forecasting of machine learning, and comprises the following steps: collecting basin hydrological observation site information data, basin characteristic data, basin historical hydrological data and meteorological forecasting characteristic data; dividing a large-scale basin into a plurality of sub-basins; preprocessing data, extracting runoff lag information obtained by using a convolutional neural network model, and constructing a runoff lag information database; adopting a deep learning long short-term memory model to couple the runoff lag information database, obtaining a plurality of sub-basin runoff lag information models; obtaining optimal runoff lag information parameters of the plurality of sub-basins according to a model precision evaluation index, obtaining an optimal hydrological forecasting model, and realizing accurate flood forecasting of the large-scale basin. The application makes up for the deficiency of flood forecasting under the condition of lack of hydrological data of a large-scale basin and has a strong application prospect in areas with little or no data.

The invention provides a complex watershed short-time flood response intelligent forecasting method based on satellite rainfall perception, and relates to the technical field of hydro-meteorological monitoring and disaster prevention and reduction. The method comprises the following steps: acquiring satellite rainfall data of a target drainage basin observed by a multi-channel satellite, and preprocessing the satellite rainfall data; inputting the preprocessed satellite precipitation data into a short-time precipitation prediction model, and outputting a short-time satellite precipitation prediction result; and inputting the short-time satellite precipitation prediction result into a semi-distributed hydrological model, simulating and generating a flood response process of a watershed scale, and outputting predicted flood process data. According to the method, the precision problem of short-time rainfall prediction of the complex drainage basin is solved, and the timeliness and stability of flood prediction are improved.

The application discloses a kind of strong human activity influence basin flood forecasting method based on hybrid model, first, the distributed hydrological model of combination sub-basin division and masking method is constructed and is rated;Again, with the characteristics of hydrological model simulation error and multi-step length basin average precipitation based on river channel propagation time, error correction model is constructed using Bayes optimization LSTM;Finally, real-time prediction is realized by coupling two models.It is proved by Lanxi basin verification that the Nash efficiency coefficient of hybrid model is significantly improved compared with traditional model, the relative error of flood peak is less than or equal to 8%, the time difference of peak appearance is less than or equal to 2 hours, and the accuracy is stable within 12 hours forecast period.The application does not need long series of continuous uninterrupted historical data as support, only needs the data during the period of flood, and is suitable for hour scale flood forecasting in data-deficient strong human activity basin, provides efficient and reliable technical means for flood control and disaster reduction, and has wide engineering application prospect.

This invention discloses a flood forecasting method for each flood event considering deep learning coupled with similar inflows, belonging to the field of hydrological forecasting technology. The method acquires a set of target floods and historical floods to be forecasted. Based on the feature vectors of each historical flood in the target flood and historical flood sets, it calculates the mechanistic similarity weight between the target flood and historical floods. It calculates the number of effective samples based on the similarity weights, adaptively filters and calibrates the sample set, and uses a perturbation gating mechanism to eliminate periods of human interference, constructing a weighted objective function to calibrate the model parameters. Using a data-driven model configured with a differentiable time-shifted layer, it corrects the amplitude and aligns the peak time of the preliminary forecast sequence of the physical model. This invention solves the problems of traditional similarity matching neglecting physical mechanisms and deep learning's difficulty in correcting phase errors, thus improving the accuracy and robustness of flood forecasting.

This invention belongs to the field of hydrological forecasting technology, specifically relating to a flood forecasting method based on ensemble Kalman smoothing data assimilation. This method is based on a conceptual hydrological model and uses real-time flow observation information to dynamically correct the model's state variables. It is applicable to scenarios such as basin flood process simulation, real-time forecasting of reservoir inflow floods, and flood control scheduling. Specifically, based on the Xin'anjiang model, this method constructs a data assimilation framework coupling the Xin'anjiang model with ensemble Kalman smoothing. It uses real-time flow observations to dynamically backtrack and correct key state variables of the model, and combines a bias-corrected Gaussian error model to correct state disturbance deviations constrained by physical boundaries, thereby improving the accuracy of flood process simulation and real-time forecasting. This method can also be optimized by combining different state update methods, ensemble size, backtracking steps, and observation weight settings to form an optimal data assimilation scheme suitable for real-time flood forecasting.

The application relates to the technical field of water conservancy management, in particular to a water conservancy informatization management method and management system based on digital twinning technology. The method comprises the following steps: a digital twinning model covering a target region is constructed; through an Internet of Things sensing network and remote sensing and other means, multi-source data is collected in real time and is synchronized with the model; the multi-source data is used to calculate the regional flood storage capacity composed of soil water storage capacity, river channel storage capacity and surface water body storage capacity; flood evolution simulation deduction is carried out based on regional weather forecasts and the flood storage capacity; and multi-level early warnings are generated and issued according to forward-looking indexes such as predicted flood storage capacity breakthrough time and inundation range. The application solves the problems of the prior art, such as dependence on static capacity evaluation and early warning reaction lag, quantifies the real flood storage capacity of the region dynamically and integrally, significantly improves the accuracy of flood forecasting and the advance amount of early warning, and provides strong scientific support for accurate flood control decision-making.

A flood rapid evolution and inundation simulation method based on a 1D-CNN algorithm, first, a two-dimensional hydrodynamic model of the research area is constructed, and the model roughness parameters are calibrated and verified; second, the two-dimensional hydrodynamic model is run to simulate the flood evolution and inundation simulation process under different historical working conditions, and the inundation water depth simulation results output by the model are extracted; then, the input and output structure of the 1D-CNN model is determined, and the training, verification and test sample sets are generated, the flood rapid evolution and inundation simulation model based on the 1D-CNN algorithm is established, and the internal weight parameters of the model are trained and optimized; finally, the application effect of the 1D-CNN model is tested, the simulation results of the two-dimensional hydrodynamic model are taken as the benchmark, the flood inundation simulation and prediction effect of the 1D-CNN model established and trained are compared and analyzed, and the accuracy and timeliness of the 1D-CNN model are evaluated. The present application can effectively solve the problems of long calculation time, large consumption and poor stability of the traditional two-dimensional hydrodynamic model, quickly and accurately simulate the flood evolution and inundation situation, meet the application requirements of real-time flood forecasting, and provide technical support for flood early warning and simulation work.

The invention discloses a flood classification and identification method based on flood feature analysis, and belongs to the technical field of hydrological forecasting and flood control and disaster reduction. According to the method, for the problem that a traditional hydrological model is unstable in performance when processing different types of floods, feature indexes of historical flood events are extracted through a system, advanced dimension reduction, clustering and recognition technologies are combined, accurate classification and real-time recognition of the flood events are achieved, and therefore differentiated parameter sets are provided for the hydrological model; and the accuracy and adaptability of flood forecasting are remarkably improved. The method not only reduces the influence of hydrological process heterogeneity on forecasting precision, but also provides more reliable technical support for flood control and disaster reduction decision-making. And taking M basin flood forecasting application as an example, the reasonability and effectiveness of the method are verified.

A continuous flood routing forecasting and operation method and system for a multi-blockage long-river system reservoir group. The method comprises: on the basis of upstream-downstream hydraulic connections and river channel composition characteristics, analyzing and quantitatively characterizing the physical structure of a cascade reservoir group system, and disassembling a multi-blockage long-river system into a river-reservoir system consisting of river channels, reservoirs and basic river channel units; generalizing upstream-downstream relationships in each component unit of the system, and quantifying waveform characteristics thereof, so as to establish flood routing process equations for upstream and downstream of a dam thereof; and on the basis of historical operation schemes of reservoirs in the component units, extracting and quantifying operation rules to realize dynamic mutual feedback calculation between operation processes and flood routing under different operation modes, and sequentially connecting the component units in series, so as to implement continuous calculation of the multi-blockage long-river system. A flood routing process under the impact of reservoir operation is quantified to realize continuous automatic calculation of a multi-blockage long-river system, thereby providing important technical support for rapid and accurate flood forecasting of basin floods under the impact of water engineering projects.

This invention discloses a method for constructing a distributed flood forecasting and scheduling model based on sub-basins, specifically relating to the field of flood forecasting model optimization technology. It addresses the problem of spatiotemporal heterogeneous local prediction failure in existing flood forecasting models due to neglecting parameter sensitivity. By dividing the target basin into sub-basins and extracting hydrogeographical feature parameters, a cross-basin hydraulic facility topology network is constructed to dynamically correct river evolution boundary conditions. The spatial heterogeneity of parameter sensitivity is quantified using the Sobol index method, and sensitive anomaly response zones are identified by combining historical flood event distributions. Spatial lag correction is applied through the river topology network to generate parameter sensitivity level classifications. A dynamic weight matrix is ​​constructed, and regional parameter calibration is performed using both global optimization and local adjustment strategies. The hydraulic balance constraints of the calibration results are verified based on river topology relationships, and a regionalized and graded flood discharge scheduling scheme is generated after iterative correction, significantly improving forecasting capabilities in sudden flood scenarios.

The application discloses a mountainous small watershed flood forecasting method based on rainfall scenario identification and terrain adaptation, belongs to the technical field of hydrology, selects a plurality of typical small watersheds with rich rainfall runoff observation data, constructs a mountainous small watershed rainfall sample set with multi-dimensional characteristics, and divides the rainfall sample set into sample subsets of multiple rainfall scenarios; the average flow velocity of the water flow in the flood process is analyzed; the characteristics of the underlying surface of each typical watershed are counted, the main underlying surface factors affecting the average flow velocity of the watershed are obtained through dimension reduction screening, and the correlation function between the underlying surface characteristics and the average flow velocity of the watershed in different rainfall scenarios is fitted; the spatial distribution of the average flow velocity of the unrecorded mountainous small watershed is batch calculated; the rainfall scenario most similar to the rainfall event in the prediction period is intelligently identified and predicted, and the concentration unit line in the rainfall scenario is automatically recommended; the runoff of the watershed is calculated based on the rainfall, and the flood process of the outlet of the batch mountainous small watersheds in the prediction period is calculated.

The invention relates to the technical field of hydrological forecasting, and discloses a Yellow River Ningmong river reach ice forecasting method based on river hydrothermodynamics, which comprises the following steps: monitoring the flow of a river reach in a research area in real time, constructing a water flow evolution model, and carrying out dynamic evolution of the ice flow of the river reach to provide flow input; constructing a water temperature calculation model to obtain the water temperature of the river reach, and when the water temperature of the river reach is reduced to zero DEG C, obtaining a runoff date; constructing a flow ice density calculation model to obtain the river reach flow ice density for judging the river reach river sealing state and the river sealing date of the research area; when the river reach of the research area is in a river sealing state, constructing an ice thickness calculation model to obtain the ice thickness of the river reach; according to the ice thickness of the river reach, a river opening forecasting model is constructed, and river opening judgment indexes are constructed by introducing river condition factors and used for judging the river opening state and the river opening date of the river reach in the research area; according to the method, the river section ice evolution process can be dynamically simulated and forecasted, and the forecasting precision is also improved.

The invention relates to the technical field of flood forecasting and artificial intelligence, and is particularly suitable for a hydrological disaster early warning scene in a multi-climate area. The invention provides a multi-factor collaborative modeling adaptive LSTM network flood forecasting method based on hydrological mechanism difference of multiple climate regions and the current situation of frequent occurrence of extreme events. According to the method, through multi-source data preprocessing, climate-topographic feature cooperative calculation, adaptive gating adjustment, cross-region migration optimization and forecast result correction, high-precision forecast of flood in different climate regions is realized; the core of the method is to construct a climate-terrain-extreme event linkage feature system and a dynamic gating mechanism, solve the problems of poor adaptability and inaccurate extreme event prediction of a traditional model, and provide technical support for intelligent water conservancy disaster prevention and reduction.

The application discloses a flood forecasting method and system based on big data and artificial intelligence, and relates to the technical field of hydrological forecasting.The method comprises the following steps: using seepage pressure monitoring data, displacement monitoring data, stress monitoring data and vibration monitoring data to respectively calculate seepage regulation indexes, displacement regulation indexes, stress regulation indexes and vibration regulation indexes, and constructing a flood season regulation characteristic vector; according to the flood season regulation characteristic vector and historical flood data, a flood season objective function and a flood season constraint condition are established, and a flood process forecasting model is constructed through deep learning training; based on the flood process forecasting model, real-time hydrological data is input, and a flood forecasting result is output. The application innovatively converts reservoir multi-source monitoring data into quantitative regulation indexes, effectively solving the problem that the flood process obtained by the traditional forecasting method cannot meet the actual flood control demand when extreme weather or special working conditions are encountered.

The application discloses a kind of river and lake regulation system flood evolution intelligent prediction method based on dynamic storage and discharge relationship, comprising: constructing the space-time dynamic correlation graph structure reflecting the hydraulic connection of basin, mapping multiple-source hydrological data to generate the model input set of unified space-time scale;Establish an aggregated reservoir parameterization structure model, including the dynamic water level-storage relationship driven by inflow variation characteristics, and the composite water level-flow relationship integrating the time lag effect and backwater jacking feedback;Construct flood regulation equation set, according to the real-time time lag state in the composite relationship, switch between different algorithm channels to solve the equation set using adaptive solving mechanism;Use real-time observation data to correct model state and perform rolling prediction.The application effectively solves the problems of difficult to describe the dynamic characteristics of storage and discharge relationship in complex river and lake system, unstable solution of non-same phase evolution process and difficult to determine high-dimensional parameters, and improves the continuity and physical consistency of flood prediction.