86 results about "Flood forecast" patented technology

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention discloses a hydrological forecasting method based on multi-feature combination and a Transform model. The method comprises the following steps: acquiring measured data of a target watershed hydrological station, building a physical hydrological model, acquiring output data and derivative feature data of the physical hydrological model, and integrating to obtain basic hydrological data; creating enhanced hydrological physical features, and forming a multi-dimensional original feature pool; constructing a plurality of combination strategies based on the basic hydrological data and the multi-dimensional original feature pool; capturing a long-term dependency relationship of the hydrological time sequence by using an improved Transform model; and the model performance is improved through automatic hyper-parameter optimization. According to the method, the influence of different input feature combinations on the flood forecasting precision is highlighted, and effective technical support is provided for water resource management and flood control and disaster reduction.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting error real-time correction method and system, and the method comprises the steps: obtaining data under a unified time reference, calculating a water purification head, and obtaining the instantaneous flow; inquiring the theoretical efficiency, calculating the actual measurement efficiency, and carrying out difference calculation to obtain an electricity-water efficiency residual error; evaluating the electricity-water efficiency residual error to obtain a stability index; judging an operation phase, and calculating the confidence coefficient of the operation phase according to the stability index; generating a correction vector and correcting the uncorrected process line to obtain a corrected process line; projecting the correction process line according to a preset engineering feasible region to obtain first correction output; performing online checking and incremental correction on the first correction output to obtain final correction output; and updating the model initial state and the boundary condition of the next time window according to the final correction output. According to the method, the problem of error accumulation caused by phase misjudgment under the hysteresis effect and field disturbance of a traditional method is solved, and the real-time accuracy of flood forecasting is remarkably improved.

The invention relates to the technical field of urban hydrological simulation and flood forecasting, and particularly discloses a comprehensive unit line improvement method applicable to an urbanized region, which comprises the following steps: S1, constructing an urban rainstorm runoff model, and simulating the flow process of a drainage basin outlet section under rainstorm in different recurrence periods; s2, calculating the flow process of the drainage basin outlet section under the same design rainstorm, and reconstructing and converting the flow process into a centralized flow process; s3, distributing the centralized flow process into sub-flows of a water outlet and a river channel section; s4, on the basis of the actual maximum drainage capacity of the drainage port, storage and drainage response correction is carried out on the sub-flow process of the drainage port, and a time sequence flow process considering drainage capacity constraint is formed; s5, calculating the segmented delay of the propagation time lag to the corrected flow, and obtaining an improved total flow process; the improved urban rainstorm basin outlet flow simulation is realized by performing space-time reconstruction, space distribution, drainage capacity constraint correction and propagation time delay delay on the flow process.

The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.

The invention relates to the technical field of hydrological simulation and flood forecasting, and particularly discloses a watershed runoff simulation method and device with a graph neural network fused with hydrological priori knowledge, and the method comprises the steps: carrying out the grid division of a target watershed, and building a watershed graph structure in combination with the hydrological priori knowledge; introducing a trainable weight into the graph neural network model, and performing time delay weighting based on a spatial distance to obtain a fusion network model; the meteorological driving data, the rainfall data and the drainage basin attributes serve as input, the convergence process is simulated through the fusion network model, and the predicted downstream runoff volume is obtained; and comparing the predicted downstream runoff volume with the actually measured downstream runoff volume, and optimizing all learnable parameters of the fusion network model. According to the method, the defects that a traditional physical model is large in calculation amount and long in consumed time are overcome, rapid simulation of the runoff process is achieved, and the accuracy and reliability of a data driving model in a runoff simulation task are improved by fusing hydrological priori knowledge and introducing physical constraints.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

A flood similarity intelligent analysis method based on multi-mode Transform and comparative learning comprises the steps that firstly, static features are processed through a grouping full-connection network, and the characterization capacity is enhanced through feature specific transformation; a CNN-Transform-Attention hybrid network is constructed to extract dynamic process line features, a convolutional layer captures local morphological features, a Transform encoder captures a global time sequence dependency relationship through a self-attention mechanism, key hydrological stages are adaptively focused through an attention weight, multi-modal features are adaptively integrated by adopting a gating fusion mechanism to generate unified embedded representation, and the dynamic process line features are extracted by adopting a convolutional neural network (CNN)-Transform-Attention hybrid network. A comparison learning and difficult sample mining strategy is introduced, discriminative characterization is learned in a low-dimensional embedding space through a comparison loss function training model based on the Euclidean distance, and the similarity is mapped into an interpretable probability of a [0, 1] interval; the technical problem that multi-source heterogeneous features are insufficient in utilization is effectively solved, accurate quantification and interpretable evaluation of flood similarity are achieved, and technical support is provided for flood forecasting, historical flood matching and flood control dispatching decision making.

The invention relates to the technical field of flood forecasting, and discloses a flood forecasting error correction method based on system differential response, and the method comprises the following steps: eliminating abnormal interference components of flood forecasting time series data of a target area, and obtaining purified time series data; constructing a differential response association map by taking the factors as nodes and the association strength as edge weights according to the association strength of the influence factors in the purified data; comparing the forecast data with the actual monitoring data to obtain initial error data; according to an atlas response conduction path, splitting the initial error into an error contribution component set according to an influence factor response weight; in combination with differential response characteristics of each factor, the forecast data is corrected in a targeted manner to obtain preliminary correction data; based on a flood routing basic rule, performing consistency verification on the preliminary correction forecast data, and if the preliminary correction forecast data accords with the rule, generating final correction forecast data; according to the invention, the flood forecast error correction efficiency can be improved.

The invention provides a flood forecasting method and device based on an intelligent optimization neural network, and the method comprises the following steps: obtaining original hydrological time series data, and carrying out the variational mode decomposition of the original hydrological time series data based on an optimal penalty factor alpha and an optimal decomposition number K, and obtaining K intrinsic mode functions; dividing the K intrinsic mode functions into high-frequency intrinsic mode functions, intermediate-frequency intrinsic mode functions and low-frequency intrinsic mode functions based on the center frequency of each intrinsic mode function in the flood flow prediction network, and fusing the high-frequency intrinsic mode functions, the intermediate-frequency intrinsic mode functions and the low-frequency intrinsic mode functions to obtain local time sequence features; and fusing the global time sequence features and the local time sequence features to obtain global-local fusion features, and inputting the global-local fusion features into a full connection layer to obtain a predicted flood flow value. According to the scheme, the optimal penalty factor alpha and the optimal decomposition number K are globally and automatically optimized through the optimization algorithm, and low efficiency and deviation caused by manual parameter adjustment are avoided.

The application belongs to the technical field of cross between artificial intelligence and hydrological prediction, and provides a flood prediction method based on a time sequence large model, which comprises the following steps: firstly, obtaining multivariate hydrological observation data of a target river basin and preprocessing to construct a multivariate time sequence matrix; secondly, dividing the matrix into a plurality of local time sequence blocks in the time dimension through a sliding window; thirdly, constructing an extreme value feature extraction module containing a maximum value operator and a first-order difference operator, extracting a local maximum value vector and a first-order difference absolute value vector for each local time sequence block; fourthly, splicing the two extracted vectors and the original local time sequence block in the feature dimension to obtain a reinforced block sequence; and finally, mapping the reinforced block sequence into input word units and inputting the input word units into a time sequence large language model for attention weight distribution and prediction, and outputting a hydrological flow prediction sequence of a future time window. The application can retain extreme value features, overcome cliff effect, and improve flood peak prediction accuracy.

The application discloses a flood forecasting method fusing a physical mechanism and a conditional denoising diffusion model, and comprises the following steps: obtaining multi-source hydrological data of a basin to be forecasted; driving a preset hydrological model to execute multi-stage sequential parameter optimization to obtain a basic forecasting sequence and an initial residual sequence; extracting multi-dimensional conditional variables based on the hydrological data and the initial residual sequence, and using a multi-dimensional evaluation criterion to perform dimension reduction screening to obtain a fusion conditional vector; inputting the fusion conditional vector as a guide condition into a pre-trained conditional denoising diffusion model, performing iterative denoising sampling through a multi-scale denoising operator to generate a forecasting residual probability sequence set; and coupling the basic forecasting sequence and the forecasting residual probability sequence set to obtain deterministic forecasting and probabilistic forecasting results of the basin to be forecasted. The application realizes a leap from single-point forecasting to probabilistic distribution deduction, can accurately obtain a flood deviation boundary under a changing hydrological environmental condition, and enhances the reliability of disaster prevention and mitigation decision-making.

A non-invasive correction method for flood forecasting models includes the following steps: S10: obtaining the measured rainfall, measured flow, and flow calculation process for the time period to be corrected; S20: analyzing the deterministic coefficient of the calculated flow process; S30: analyzing and correcting the length of the reserved freedom period; S40: calculating the comprehensive correction characteristic process; S50: performing a dynamic input + feedback output comprehensive correction to obtain a corrected deterministic coefficient; S60: determining whether the corrected deterministic coefficient has improved. The present invention provides a non-invasive correction method for flood forecasting models. This method, based on a dynamic input + feedback output comprehensive correction mode, can rapidly and in real time correct the errors of the forecast model for various error scenarios without intrusively modifying the original forecast model, thereby ensuring the long-term stability and reliability of the forecast model.

The invention discloses a cascade reservoir joint compensation scheduling method considering flood sources and reservoir capacity levels, and belongs to the field of reservoir joint flood control scheduling. In order to solve the problems that a traditional scheduling strategy is poor in adaptability and a reservoir impounding role is fuzzy, the method is implemented through the following steps that the range of cascade reservoirs included in scheduling is determined, and engineering characteristic data such as flood control water levels and flood control high water levels are collected; calculating the amount of flood needing to be retained and stored based on the flood forecasting process of the downstream flood control point; a control reservoir and a matching reservoir are divided according to the hydraulic topological relation, the impounding capacity is distributed by combining the flood volume proportion of each reservoir and the remaining flood control reservoir capacity proportion, full-reservoir impounding distribution is completed through secondary distribution and recursion, the dispatching effect is checked, and the flow regulation and control factors are dynamically adjusted. The method is simple, practical, reasonable, reliable and capable of quickly adapting to different water regimes and work conditions, accurately positioning the reservoir retaining role, improving the overall flood retaining benefit of the cascade reservoir group, conforming to actual dispatching characteristics, being easy to popularize and providing scientific support for real-time dispatching decision of a reservoir operation management department.

The invention discloses a drainage basin collaborative forecasting optimization system and method based on water conservancy big data, and belongs to the technical field of water conservancy forecasting. The method comprises the steps of setting drainage basin monitoring stations and deploying Internet of Things sensors, dividing monitoring areas and synchronizing timestamps, binding station identifiers to generate hydrological characteristic flows, constructing a full-drainage-basin datamation matrix, calculating state differences to update matrix elements, and finally generating a downstream station collaborative forecasting matrix to determine a flood forecasting range. The system comprises a monitoring deployment and time synchronization unit, an identifier binding and feature flow generation unit, a data capture and matrix construction unit, a difference calculation and matrix updating unit and a collaborative forecast and alarm generation unit. According to the invention, while the time-space consistency of the data is ensured, the coincidence between the collaborative forecast relevancy and the actual flood propagation path can be enhanced, the accuracy and pertinence of the boundary effect of the alarm range are improved, and the response time from data acquisition to alarm generation is shortened.

The invention belongs to the technical field of flood forecasting, and provides a flood forecasting method and system based on historical data, and the method comprises the steps: carrying out the comparative analysis of a historical hydro-meteorological data set of a forecast drainage basin in a plurality of historical years and an actual hydro-meteorological data set collected in the forecasting process, screening out a target data set, and carrying out the calculation of the target data set; hydrological models used during flood forecasting in historical years are processed and analyzed, the hydrological models conforming to a target data set are found, the target data set is used for verifying and analyzing the to-be-tested models, the target hydrological models are screened from the to-be-tested hydrological models according to verification and analysis results, and the to-be-tested hydrological models are subjected to flood forecasting according to the screened target hydrological models. According to the method, the model weight is given to the obtained target hydrological model, and the target hydrological parameters are output in combination with the actual hydrological meteorological data set, so that the accuracy of the hydrological parameters output during flood forecasting is improved, and the accuracy of flood forecasting is further improved.

The present invention provides a method and system for intelligent site selection for forecast monitoring sites in a small reservoir basin, belonging to the field of hydrological monitoring technology. The present invention comprises the following steps: inputting the parameters required by the flood forecast model, performing parameter sensitivity analysis, and outputting a parameter weight matrix; generating a heat map of spatial data demand in the basin based on the output parameter weight matrix; determining a first set of candidate sites based on the heat map; based on the first set of candidate sites, superimposing geographic constraints, filtering out inaccessible points, and obtaining a second set of candidate sites; based on the second set of candidate sites, using an adaptive genetic-particle swarm hybrid algorithm to randomly generate a preset number of site combination schemes, and outputting the optimal site coordinate set through continuous iteration. This solves the problems of low coverage in key areas, high construction costs, and low data reporting rates.

This invention provides a computer vision-based method for optimizing and intelligently monitoring urban flooding simulation, belonging to the field of flood model monitoring technology. The method includes: determining the study area and establishing a one-dimensional hydrodynamic model of the water network based on initial modeling data; using computer vision, segmenting and extracting land use data of the study area using the SegNet and U-Net algorithms, reshaping the underlying surface, constructing new land use data, and calculating new uncertainty parameters; comparing and analyzing the performance of the one-dimensional hydrodynamic model before and after updating the uncertainty parameters; and constructing a one- or two-dimensional surface coupling model combining the integrated flood forecasting model ITF-FLOOD with land surface flux. This one- or two-dimensional surface coupling model is used to simulate the depth and area of ​​surface water accumulation within the study area, completing the optimization and intelligent monitoring of urban flooding simulation. This invention can improve the simulation accuracy of urban flooding models and provide a more intelligent and convenient monitoring method for flood-prone areas.

The present invention discloses a method for analyzing dam seepage stability based on flood forecasting, which relates to the technical field of water conservancy project safety monitoring. The method includes S1, a data acquisition layer integrating a flood forecasting system and a seepage monitoring system, wherein sensors in the data acquisition layer acquire flood forecasting data and seepage monitoring data; S2, a spatiotemporal coupling interface of a dynamic fusion layer that aligns flood forecasting data with seepage monitoring data in spatiotemporal order, and generates dynamic driving variables for seepage field boundary conditions based on a flood evolution model and geographic information system spatial interpolation technology. The present invention achieves spatiotemporal alignment of flood forecasting data and seepage monitoring data and generation of dynamic driving variables through the spatiotemporal coupling interface, solving the problem in traditional analysis that boundary conditions are static or lagging and cannot reflect the real-time flood evolution process. The method enables the boundary conditions for seepage field calculation to be dynamically updated with flood forecasts, thereby improving the timeliness and accuracy of model calculations.

The invention discloses a water conservancy remote sensing data low-dimensional feature efficient extraction and prediction method based on deep metric learning, and the method comprises the steps: focusing multi-source data driving and integrated model optimization, collecting flood-related multi-source data, and carrying out the cleaning and standardization processing to construct a data set; a depth metric learning model containing time sequence and space feature extraction branches is built, and a high-discrimination feature vector is output by combining the target function with log-ex approximation; splicing the features and fusing the features into manual time sequence features to form a fused feature matrix; constructing an integrated base model set, dynamically adjusting parameters by means of intra-class / inter-class distances, and performing time sequence cross validation training; constructing a second-level training set by using a base model prediction result, training a second-level model learning weight, and establishing a final integrated regression prediction model; and inputting real-time data to obtain a forecast result, and carrying out iterative optimization according to the evaluation index. Through small-watershed and large-watershed tests, the method adapts to watersheds of different sizes, the flood forecasting precision and stability are effectively improved, and reliable technical support is provided for flood control decision making.

The invention belongs to the technical field of flood forecasting, and particularly relates to a method for calculating post-wave water level by sudden flood forecasting flow, which comprises the following steps: S1, acquiring hydrological observation and flow forecasting data, and determining a calculation control node; s2, calculating a first adaptive flow and a first water level before the node 2; s3, calculating a first adaptive flow and a first water level behind the node 2; s4, calculating a second adaptive flow and a second water level before the node 2; s5, calculating a second adaptive flow and a second water level after the node 2; s6, according to the first adaptive flow, the first water level, the second adaptive flow and the second water level, the water surface gradient process is calculated; and S7, outputting a post-wave water level forecast calculation result according to preset data submission ending time. According to the method, two post-wave flood indicating flow hydrographs and corresponding water level hydrographs can be generated, and the technical level of post-wave water level forecasting of the flood is effectively improved.

The application provides a set forecast precipitation information space-time error correction method and system based on image super-resolution, and relates to the technical field of precipitation forecast.The application corrects the space-time error of set forecast precipitation information based on the image super-resolution idea and a deep learning algorithm, improves the spatial resolution of set forecast precipitation information, and improves the forecast precision.The method has outstanding advantages in improving the set forecast precipitation space-time structure similarity and the precision of rain or no rain certainty classification forecast compared with the traditional statistical post-processing method, and provides high-precision data support for the application of set forecast precipitation information in flood forecast business.

The application discloses a hydrological model real-time correction method based on a reinforcement learning A2C algorithm, and the method comprises the following steps: step 1, obtaining and preprocessing basic data of a research area; step 2, constructing a real-time correction model based on the reinforcement learning A2C algorithm; step 3, training and verifying the real-time correction model; and step 4, correcting rainfall in real time by using the real-time correction model. By coupling the reinforcement learning A2C algorithm and the hydrological model, the application solves the problems of slow convergence and easy falling into local optimization of a traditional method, and realizes high-precision and self-adaptive real-time correction of flood forecasting. The method disclosed by the application significantly improves the accuracy and dynamic response capability of flood forecasting, and provides reliable technical support for flood control and disaster reduction and water resource management.

The invention discloses a hydrological mechanism model parameter optimization method based on MoE, and the method comprises the steps: carrying out the session division and standardization of the historical data of a drainage basin at a data preprocessing stage, and extracting the key hydrological features of each session; in the parameter analysis stage, hydrological mechanism model parameters are layered by adopting a global sensitivity analysis method to form high, medium and low sensitive parameter sets; in a hybrid expert model construction stage, a multi-path expert network and a gating mechanism are introduced, and differential optimization and dynamic fusion of different sensitive level parameters are realized in combination with a feature enhancement module. According to the method, the efficiency and uniqueness of parameter calibration of an existing hydrological mechanism model are improved, and the precision and stability of the method in flood forecasting are remarkably enhanced.

The application belongs to the technical field of hydrological prediction, and particularly discloses a runoff prediction model considering extreme runoff and a construction method and system thereof, which comprises the following steps: collecting and preprocessing prediction factors of a research area to form a prediction factor set; determining the contribution degrees of the prediction factors and obtaining N prediction factors with the highest contribution degrees through preliminary screening; sequentially eliminating the N prediction factors from small to large contribution degrees and respectively inputting the preliminary prediction model for training and testing; selecting a group of prediction schemes with the highest Nash efficiency coefficient as a common model for the test results; simultaneously fitting the predicted runoff and the actual runoff output by the preliminary prediction model in the range exceeding the extreme threshold of runoff, and selecting a group of prediction schemes with the best fitting as an extreme value model; and coupling the common model and the extreme value model to obtain a comprehensive runoff prediction model. The application can effectively consider long-term runoff prediction and extreme flood prediction, and is suitable for popularization and use.

This invention provides a lightweight, edge-side LSTM-based integrated flood forecasting method based on ONNX. It acquires flow data and processes missing and outlier values, performs sliding window sampling and standardization, and constructs and trains an LSTM flood forecasting model based on the processed sample data. The trained LSTM flood forecasting model is then converted into an ONNX model and optimized through computational graph optimization, constant folding, quantization optimization, and hardware adaptation optimization to obtain a lightweight flood forecasting model. This lightweight flood forecasting model is then deployed on edge devices to perform local rolling inference on real-time collected flow monitoring data and output flood forecast results. This invention enables closed-loop operation of data acquisition, local inference, and forecast output on the edge, reducing model size, memory usage, and power consumption, and improving the real-time performance and reliability of flood forecasting in remote areas and scenarios with weak networks.

This invention provides a method for determining hydrological model parameters in data-free areas by combining dynamic and static features. Through multi-dimensional feature construction, improved dimensionality reduction methods, precise similarity partitioning, and parameter transfer, the method effectively improves the accuracy of determining hydrological model parameters in data-free areas. It provides a reliable technical method for water resource management, flood forecasting, and other work in data-free areas and has broad application prospects.

The application discloses a flood forecasting method based on time-varying parameters, and relates to the technical field of flood forecasting. The method comprises the following steps: determining the correlation between a target runoff recession coefficient and a target time-varying parameter and a preset reference time-invariant parameter by considering the target time-varying parameter and the reference time-invariant parameter; constructing a time-varying parameter reservoir model based on the correlation; and finally, performing flood forecasting based on the time-varying parameter reservoir model. The method can effectively consider the time-varying runoff intensity, and can analyze the whole system from a macro perspective, and can generalize the slope confluence process into a wide and shallow open channel flow process, so that the accuracy of flood forecasting is improved.

The application discloses a kind of face, block and point scale linkage flood warning method based on river forecast water level in plain area, according to the flood face scale early warning of plain representative site, for having face scale early warning area, using GIS technology and the spatial autocorrelation of plain area water level in flood forecast process, calculate submerged grid and depth, carry out block, point scale early warning.The method is a kind of simple, fast method.It includes the following steps: collecting plain area basic data;A kind of face, block and point multiscale linkage early warning mechanism;Compare regional representative site forecast water level and characteristic water level, dike elevation, and carry out face scale flood risk early warning;With face scale early warning area as unit, obtain regional forecast water level face data, generate the submerged grid and water depth of regional unit, and carry out block scale early warning;With face scale early warning area as unit, in combination with submerged face, submerged water depth and flood risk point data, utilize spatial analysis technique to carry out point scale early warning.

The invention discloses a flood forecasting method based on a time-varying parameter, and relates to the technical field of flood forecasting, by considering a target time-varying parameter and a preset reference time-invariant parameter, an incidence relation between a target runoff regression coefficient and the target time-varying parameter and an incidence relation between the target runoff regression coefficient and the reference time-invariant parameter are determined; according to the method, a time-varying parameter reservoir model is constructed based on the incidence relation, and finally flood forecasting is performed based on the time-varying parameter reservoir model, so that the time-varying runoff production intensity can be effectively considered, overall analysis is performed from the macroscopic perspective of the system, and the slope confluence process is generalized into a wide and shallow open channel water flow process, thereby improving the accuracy of flood forecasting.