114 results about "Flood forecast" patented technology

The invention relates to the technical field of data evaluation processing, in particular to a rural waterlogging area waterlogging risk grade evaluation and early warning method based on multi-source data. The method comprises the following steps: acquiring geographic information, hydrological and flood data and flood control and drainage engineering data of a rural waterlogging area, and performing multi-source data fusion to obtain rural flood multi-source data; and performing river flood overflow calculation on the rural flood multi-source data to obtain river flood overflow data. According to the invention, through a data processing technology, a mode identification technology and a deep learning technology, rural multi-source feature data is subjected to fusion analysis, and risk level evaluation of a waterlogging area and a waterlogging sheet in a rural area is realized; a flood real-time forecasting and early warning model is constructed, when rainstorm occurs or a meteorological department issues rainstorm early warning, the rural flood flooding range is quickly predicted, and a risk avoiding transfer route is planned.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention discloses a dam seepage stability analysis method based on flood forecasting, and relates to the technical field of hydraulic engineering safety monitoring, comprising the following steps: S1, integrating a flood forecasting system and a seepage monitoring system into a data acquisition layer, and acquiring flood forecasting data and seepage monitoring data by a sensor of the data acquisition layer; s2, performing space-time alignment on the flood forecast data and the seepage monitoring data through a space-time coupling interface of the dynamic fusion layer, and generating a dynamic driving variable of a seepage field boundary condition based on a flood routing model and a geographic information system spatial interpolation technology; through the space-time coupling interface, space-time alignment and dynamic driving variable generation of flood forecast data and seepage monitoring data are achieved, the problem that the real-time flood routing process cannot be reflected due to the fact that the boundary condition is static or lagged in traditional analysis is solved, the seepage field calculation boundary condition can be dynamically updated along with flood forecast, and the real-time flood routing process cannot be reflected. And the timeliness and accuracy of model calculation are improved.

The invention discloses a multi-project flood control and disaster reduction method based on a four-pre-coupling model, and the method comprises the steps: constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction forecasting model, constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction early warning model, and constructing a reservoir-river channel-dike multi-project system flood control and disaster reduction rehearsal model. A flood control and disaster reduction plan model of a reservoir-river channel-dike multi-project system is constructed, a monitoring network is planned and designed according to a certain rule, and four-pre dynamic coupling including forecasting, early warning, rehearsal and pre-warning is carried out. Compared with the prior art, the method comprehensively considers the synergistic effect and influence restriction relation of flood control processes of a reservoir, a river channel and a dike, and realizes chain type dynamic connection of four pre-links of flood control and disaster reduction through a multi-engineering system coupling flood control four-pre dynamic coupling model; and flood forecasting precision, flood control and disaster reduction decision scientization and flood risk management refinement are realized.

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 belongs to the technical field of data processing, and particularly provides a flood probability forecasting method and device based on artificial intelligence, and the method comprises the steps: obtaining downscaled long-series meteorological observation data through employing a statistical downscaling model based on ground observation data and satellite remote sensing inversion data; constructing a deep learning model considering the hydrological process; reconstructing to obtain a long-series daily runoff process of the scarcity data drainage basin, and extracting a flood event by adopting an over-quantitative sampling method; adopting a plurality of artificial intelligence models suitable for flood magnitude simulation to construct a flood simulation model; based on the simulation result and the actually measured flood magnitude, a Bayesian mode averaging method is adopted to deduce the weight of each model, and an artificial intelligence-Bayesian coupling model is constructed; a Copula function is combined to construct a distribution function of actually measured flow and forecast flow, and an artificial intelligence-Bayesian coupling model is adopted to carry out flood forecasting. The method is used for solving the defect that the uncertainty and the artificial intelligence technology are not fully considered for flood forecasting.

The invention discloses a flood forecasting method and device based on multi-model cooperation and a medium, belongs to the technical field of flood forecasting and flood disaster simulation, and is used for solving the technical problems of insufficient coupling of flood forecasting models, weak visualization ability, lack of real-time interaction and non-visual information transmission in a traditional flood forecasting method. The method comprises the steps of collecting water conservancy information data of a target drainage basin; according to drainage basin characteristics of the water conservancy information data, collaborative architecture interaction under related multiple models is carried out on water conservancy factors in the target drainage basin, and a water outlet professional collaborative model is constructed; through a preset UE5 engine, performing three-dimensional modeling simulation calculation related to a flood routing process and a flood inundation range on result data output by the water professional cooperation model to obtain a three-dimensional simulation scene; based on the three-dimensional simulation scene, performing multi-dimensional information broadcasting on the real-time water conservancy data, and determining multi-dimensional simulation forecast data; and performing key index verification analysis on the multi-dimensional simulation forecast data to obtain flood control decision information.

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 discloses a flood control scheduling simulation method and system suitable for multistage series-parallel cascade reservoirs. The method comprises the following steps: collecting and sorting basic data; extracting a river network water system, dividing sub-basins, generating a topological structure based on a cascade reservoir series-parallel connection relation, and performing standardization processing; on the basis of a cascade reservoir topological structure relation, a model is constructed based on a three-layer nested loop, and reservoir entering flood and flood regulation calculation process simulation of a cascade reservoir is realized step by step from upstream to downstream; by setting schemes, cascade reservoir flood control scheduling processes under different schemes are simulated, and scheme comparative analysis and effect evaluation are carried out. According to the method, the data base plate and the water and rain condition monitoring information are fully utilized, cascade reservoirs with complex series-parallel connection relations such as series connection, parallel connection or series-parallel connection can be adapted, cascade reservoir flood forecasting and flood control dispatching process simulation is achieved, reliable guarantee is provided for scientifically and reasonably formulating a cascade reservoir dispatching scheme, and the method is suitable for large-scale popularization and application. And a technical support is provided for improving flood control and disaster reduction capabilities.

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 a flood forecasting precision improving method based on multi-model data fusion driving, relates to the technical field of meteorological numerical forecasting data fusion, and aims to solve the problems of uncertainty of a numerical mode of simulated atmosphere, defects of the mode and forecasting errors of the numerical mode. The method comprises the following steps: respectively collecting output data of different numerical weather forecast models, and normalizing the output data; selecting a weather forecast model group with representativeness and diversity to obtain a combined weather forecast model and an ensemble forecast sample; performance evaluation is carried out on the ensemble forecasting samples obtained through the evaluation indexes, and if the performance reaches a preset target, meteorological numerical forecasting data fusion of multi-model random combination is completed; and otherwise, adjusting the random combination strategy and the integration model, and carrying out iterative optimization until a predetermined target is achieved.

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 provides an intelligent site selection method and system for a small reservoir basin forecast monitoring site, and belongs to the technical field of hydrological monitoring. The method comprises the following steps: inputting parameters required by a flood forecasting model, carrying out parameter sensitivity analysis, and outputting a parameter weight matrix; according to the output parameter weight matrix, generating a drainage basin spatial data demand thermodynamic diagram; determining a first candidate site set based on the thermodynamic diagram; on the basis of the first candidate site set, geographic constraint conditions are overlaid, unreachable sites are filtered out, and a second candidate site set is obtained; and based on the second candidate site set, adopting an adaptive genetic-particle swarm hybrid algorithm to randomly generate a preset number of site combination schemes, and outputting an optimal site coordinate set through continuous iteration. The problems of low coverage rate of key areas, high construction cost and low data reporting rate are solved.

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 invention discloses a cascade reservoir distributed robust flood control optimization scheduling method considering flood forecast errors, and the method comprises the steps: building an uncertainty set of the flood forecast errors based on statistical information, and fully considering the distribution rule of the flood forecast errors; meanwhile, a distributed robust optimization scheduling model is constructed with the minimum flow maximum value of the downstream control station, and the optimal error amplification coefficient is selected with the scheduling scheme risk as the index, so that the water resource utilization efficiency can be improved on the premise of effectively controlling the flow of the downstream station; therefore, safety of downstream flood control sites is guaranteed and economic benefits are improved.

The invention discloses a complex river network flood prediction method based on data dynamic cleaning and an adaptive recurrent neural network. The method comprises the following steps: S1, collecting flood monitoring data in a research area; s2, abnormal value detection and correction are carried out on the data through a speed constraint dynamic cleaning method based on flood classification; s3, preprocessing the cleaned data by using a sliding window mechanism and a data normalization technology, and setting a plurality of prediction period windows for direct multi-step prediction; s4, constructing and optimizing an adaptive recurrent neural network model, wherein the model comprises a gating circulation unit layer and a bottleneck layer; s5, optimizing the hyper-parameters through grid search and an adaptive momentum estimation optimizer, and finally determining model configuration; s6, performing complete training and testing based on the determined model configuration; and S7, flood prediction is carried out through the trained model. The model is combined with actual monitoring data, intelligent river network flood forecasting in different forecasting periods is achieved, and the flood forecasting precision and stability are remarkably improved.

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.

The invention discloses a drainage basin flood forecasting and dispatching model parallel computing method, which is based on the tree-shaped confluence relation characteristic of a drainage basin river, and generalizes the river in the drainage basin, flood forecasting in the river and water engineering dispatching nodes. According to the drainage basin flood forecasting and dispatching model parallel computing method, dynamic allocation is carried out between a generalized river and a plurality of cores of a CPU, computing resources are utilized to the maximum extent, a reasonable speed-up ratio is achieved, the situation of computing waiting is effectively avoided, good computing performance can be achieved when computing power is insufficient, and the computing efficiency is improved. And an effective way is provided for improving the model calculation efficiency during flood prevention scheduling consultation.

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 application discloses a parameter time variation and process correction differentiable hydrological model flood forecasting method, solves the problems of traditional model interpolation distortion and difficulty in fusing mechanism and intelligent model. The method directly uses non-equidistant observation data, builds a differentiable hydrological equation to realize gradient return, and combines sensitivity analysis to realize directional coupling correction of high-sensitivity parameters and high-error runoff processes, realizes time variation prediction of high-sensitivity groundwater exchange parameters and neural network coupling optimization of runoff processes respectively; an adaptive confluence structure of quick and slow flow is adopted, combined with multi-constraint optimization and double verification to avoid overfitting and physical distortion. The application considers hydrological mechanism and data fitting effect, can accurately predict complete flood process, improves prediction accuracy and universality, and is suitable for basin flood control and water resource scheduling.

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.