359 results about "River network" patented technology

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The invention discloses a plain water network area runoff simulation method which comprises the following steps: acquiring and preprocessing multi-source data to obtain a time-space consistency hydrological data set; based on the time-space consistency hydrological data set, obtaining polder area water level data and exchange flow data by adopting an enhanced polder system dynamic model; carrying out runoff production calculation in the plain area by adopting an improved XAJ model; constructing a multi-scale river network topological structure, and executing multi-directional flow state identification and prediction of deep space-time fusion; and constructing a time-varying structure adaptive BP neural network model to realize runoff simulation of the plain water network area. According to the method, the technical problems of reverse flow phenomenon identification in a plain water network area, polder area water exchange threshold effect simulation, distribution node dynamic distribution coefficient optimization, temporary moving pump station influence correction and the like can be effectively solved.

The invention discloses a time-space diagram neural network water quality prediction method based on DEM composition, and the method comprises the steps: obtaining and processing DEM data of a target region, extracting a river topological structure in the target region, and constructing a graph structure reflecting the spatial relation of water quality monitoring stations; acquiring water quality data, preprocessing the data, and eliminating the influence of missing values and abnormal values; a water quality prediction model is constructed based on an STGCN model, the STGCN model comprises a plurality of space-time convolution blocks, and each space-time convolution block comprises a time gating convolution layer, a space graph convolution layer and a time gating convolution layer which are arranged in sequence; and detecting the water quality to be detected based on the constructed water quality prediction model. According to the method, the accuracy of water quality prediction is improved by capturing the spatial dependency relationship between the water quality monitoring stations. A river network topological structure is extracted by using DEM data, a graph structure capable of truly reflecting hydrological relation between monitoring stations is constructed, and the accuracy of water quality prediction is effectively improved by combining effective capture of the STGCN model on spatio-temporal characteristics.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses a distributed model-oriented medium and small watershed sub-watershed division method, which comprises the following steps of S1, collecting digital elevation model data, performing depression filling processing, and extracting preliminary river network information; s2, extracting an actual water system, and calculating a river network main stream grid elevation difference; s3, adjusting digital elevation model data according to the elevation difference, and optimizing a river network extraction result; s4, basin morphological parameters are calculated, and basin classification is carried out through a manifold learning method; s5, constructing a sub-basin optimal threshold calculation model, and dynamically adjusting a sub-basin division threshold; s6, flow direction calculation, confluence analysis and sub-basin division are carried out, and sub-basin boundaries are optimized; and S7, verification is carried out in combination with the distributed hydrological model, the influence of the division result is analyzed, and sub-basin division parameters are optimized. According to the method, multi-scale features, dynamic threshold optimization and hydrological network topology are fused, sub-basin division precision, boundary continuity and hydrological simulation stability are improved, and intelligent basin division is realized.

The invention discloses an adaptive neural network flood routing simulation and risk assessment method. The method comprises the following steps: step 1, generating multi-source hydrometeorological preprocessing data; 2, constructing a river network topological graph, generating node feature vectors and edge feature vectors, and forming time-space diagram input data; 3, inputting the liquid state time constant neural network to execute continuous time state updating, and generating a node hydrological state prediction vector set; step 4, injecting hydrodynamic physical consistency residual errors to generate a physical constraint hydrological state prediction vector set; 5, executing adaptive lag compensation, and generating a lag compensation hydrological state prediction vector set; 6, mapping to generate flow, water level and flood peak arrival time prediction data; and step 7, outputting a risk grading graph and early warning threshold list data. According to the invention, accurate prediction of the flood process and dynamic generation of the risk map are realized, and early warning precision and response efficiency are improved.

The invention discloses an urban inland inundation optimization transformation method and system based on a pipe network earth surface coupling hydrodynamic model, and the method comprises the steps: firstly generalizing a pipeline and a catch basin, constructing a pipe network confluence model according to a pipe network hydrodynamic method, then dividing a sub-catchment area according to a river network flood routing method, building an urban comprehensive drainage model through the sub-catchment area, and carrying out the optimization transformation of the urban inland inundation. Then establishing a surface runoff model by using a surface flood routing method and a Horton penetration method; according to the system, the function of accurately and efficiently simulating the urban inland inundation process is achieved, urban rainwater circulation is completely simulated, an urban drainage pipe network and a river channel model are integrated, and the interaction between an urban drainage system and a ground surface storage water body is truly simulated; and necessary support is provided for disaster situation rehearsal and rainwater engineering planning and layout, so that urban inland inundation points can be removed in advance, influences on production and life of people are avoided, and the method is suitable for being widely popularized and used.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

The invention discloses a method for dividing calculation units of a hydrological model in a plain water network area. The method comprises the following steps: acquiring and preprocessing multi-source data to form an integrated data set; extracting a river network skeleton based on the integrated data set and constructing a river network topological structure; identifying hydraulic engineering facilities and analyzing the connectivity of the hydraulic engineering facilities, and generating a regional connectivity feature map; and carrying out hydraulically-driven calculation unit adaptive division based on the river network topological structure and the regional connectivity feature map. According to the method, the plain water network area hydrological simulation precision can be effectively improved, and the calculation efficiency is optimized.

The invention relates to the technical field of wetland ecological restoration, in particular to a beach wetland near-natural ecological restoration method based on a hydrological connectivity threshold value, which comprises the following steps of: firstly, collecting multi-source data such as river network boundary vector data and wetland topographic data of a drainage basin, and selecting a representative remote sensing image; analyzing a hydrological sequence change trend and water body information, and calculating a hydrological situation change degree; then constructing a hydrological and hydrodynamic model, and cooperatively observing space-time evolution of the wetland; the hydrological connectivity is quantified, and the evolution relation between the hydrological connectivity and the wetland is analyzed; and finally, according to the response of wetland evolution to hydrological communication, identifying a hydrological communication threshold value, and proposing water system communication and ecological channel restoration suggestions. The method is scientific, reasonable and high in practicability, can effectively reveal the wetland evolution mechanism, provides an important reference basis for basin hydrological regulation and control and wetland ecological restoration under the influence of human activities, and has important application value in the field of wetland ecological restoration.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention relates to the technical field of water conservancy informatization and intelligent early warning, in particular to a river network water regimen big data intelligent analysis early warning system which comprises a dynamic data fusion module, a topology sensing module, an overload propagation analysis module, an intelligent early warning decision module and an early warning execution module. Wherein the dynamic data fusion module is used for collecting multi-dimensional data of a river network area in real time; the topology sensing module is used for dynamically constructing a real-time topological relation graph of the river network connectivity; the overload propagation analysis module is used for extracting an overload coefficient of each node and outputting a risk diffusion path set; and the intelligent early warning decision module is used for generating a three-level early warning instruction by combining the positions of the engineering vulnerabilities in the real-time topological relation graph. According to the method, through fusion of the multi-source water regimen data and the dynamic topological structure, linkage of accurate identification and hierarchical response of the risk path is realized, and the intelligent early warning and rapid disposal capability of the sudden water regimen of the river network is remarkably improved.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention discloses a flood classification method based on a graph attention neural network. The method comprises the following steps: constructing a flood event data set; constructing a flood map set; based on a graph attention neural network GAT, graph-level features of a flood map corresponding to each flood event are extracted; and carrying out flood classification by using a K-means clustering method based on the graph-level features of the flood graph set. According to the method, the graph neural network is introduced into the field of flood classification, deep learning modeling of drainage basin flood space correlation features is realized, multi-site synchronous monitoring flow data in a drainage basin is integrated by using the graph attention network, space-time evolution features of flood in a drainage basin river network are extracted, and the limitation of a traditional single-site analysis method is broken through. According to the method provided by the invention, the spatial heterogeneity of flood is relatively considered, and the classification result contains richer information.

The invention relates to the field of process control, in particular to a multi-target particle swarm optimization scheduling system for flood control and waterlogging removal in a plain river network polder area. The method comprises the specific implementation steps that a coupling hydrodynamic model module is constructed, the water flow exchange amount between a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model is calculated, multiple times of simulation are carried out to generate a training data set, and a neural network agent model is trained and generated; generating an initial population based on topological structure information of the plain river network and a heuristic rule by adopting a particle swarm optimization algorithm; according to external work condition information, dynamically adjusting feasible region constraints of an optimization algorithm, inputting candidate scheduling schemes conforming to the constraints into the neural network agent model, and outputting a predicted scheduling state; based on the predicted scheduling state and the weight coefficient, the fitness value of each candidate scheduling scheme is calculated, the optimal scheduling scheme is output through iterative optimization, and the decision-making efficiency, robustness and safety of the scheduling system in the face of complex working conditions and emergencies are improved.

The invention discloses a physical mechanism and deep learning coupled flood forecasting method, device and system. The flood forecasting method comprises the steps of obtaining DEM data, hydro meteorological data and drainage basin static attribute data of a target drainage basin; based on the DEM data, dividing a target drainage basin into a plurality of sub-drainage basins, extracting a river network, and calculating static attribute data of the river network, the river network comprising a plurality of river channels; inputting the hydrometeorological data, the drainage basin static attribute data and the river network static attribute data of each sub-drainage basin into a pre-trained distributed hybrid hydrological model to obtain simulated flow of a hydrological observation station, and completing flood forecasting; the distributed hybrid hydrological model is coupled with a physical mechanism and deep learning. On the premise that the distributed hybrid hydrological model has good interpretability, high-precision daily runoff and flood simulation can be achieved, and high calculation efficiency is achieved.

The invention belongs to the technical field of hydrology and water resources, and discloses a cold and dry mountainous area drainage basin runoff simulation method based on an MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data. According to the multi-source data fusion technology, a cold and dry mountainous area distributed input data set is constructed through TRMM satellite rainfall data, an MODIS LAI product and an ASTER DEM, and the rainfall space representation precision is improved by 40%. According to the model coupling mechanism, an MIKE SHE / MIKE 11 collaborative simulation framework is provided, earth surface-groundwater flow data interaction is achieved through river channel linkage, and the complex river network flood routing simulation error is reduced to 10% or below. According to the parameter partition calibration method, a drainage basin is divided into five hydrogeological regions, sensitive parameters such as a Manning coefficient and a hydraulic conduction coefficient are optimized in combination with a genetic algorithm, and the basic flow simulation precision is improved by 25%.

The invention provides a water network reconstruction method based on forest water function combination. The method comprises the following steps: identifying water system features and constructing a topological structure; carrying out multi-dimensional forest stand attribute fusion and evaluation; optimizing a water network model based on a circuit theory; the regulation and storage function of the water network is simulated through multiple paths; and constructing a water forest habitat. According to the method, a water system network topological structure is established by adopting a spectrogram convolutional network, a forest-water interaction model is constructed by fusing multi-dimensional forest stand attributes and a circuit theory, a hydrological process is simulated, a riverbed form is adjusted, a water flow dynamic state can be simulated in various situations, the regulation and storage efficiency of a water network under different paths can be evaluated, and a water system layout and a hydrological analysis result are combined to obtain a high-performance water system. The landform transformation is implemented, the problems of water-green space splitting, fragile ecological function, single greening landscape and the like are solved, the effective river network regulation and storage space of green forest land which is green at ordinary times and is water in disaster is increased, the water forest ecological landscape is built, and important tools and strategies are provided for realizing forest-water compounding and improving city toughness.

The invention discloses a riverway treatment method and system based on an image processing technology, and relates to the technical field of urban riverway treatment, the system divides a treatment area into a plurality of sub-river reaches by using a GIS, river network distribution and a DEM model, and collects video frames, water quality images and pollution feature images; through image processing, extracting a motion track, a distribution characteristic, water body color information and an abnormal color development area of the floating object, and analyzing a pollution change trend and a mutation characteristic; the water surface garbage diffusion disturbance coefficient is monitored, the water surface garbage state is judged in combination with a threshold value, and strategy response is triggered; when the water quality is abnormal, judging whether the color of the water body is normal or not through water body color difference abnormal coefficient analysis; if pollution exists, the pollution gradient change is analyzed section by section in the countercurrent direction of the river channel, a pollution source is locked, and a pollution discharge upstream inference report is generated; according to the invention, the river pollution monitoring precision and response efficiency are effectively improved, and ecological management and environmental protection are assisted.

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

The invention discloses a river network hydrodynamic model construction method and system based on large language model reasoning, and belongs to the technical field of hydrological modeling and artificial intelligence crossing, and the method comprises the steps: carrying out the filling of original DEM data, and calculating a water flow direction matrix; iteratively calculating and analyzing the optimal confluence cumulant, and extracting a river network grid; outputting a river network grid of a specified level through an instruction of specifying the level of the output river network; coding the river network grids into a format described by a natural language to train a large language model; analyzing the spatial distribution of the river channel through a large language model, and generating a topological structure diagram containing nodes and directed edges; identifying a water conservancy project through a large language model, and generating a water conservancy project object and position information thereof; according to the river network hydrodynamic model construction method and system based on large language model reasoning, the problems that a traditional method depends on manual intervention and is low in modeling efficiency are solved, and high technical innovation and practical application value are achieved.

The invention discloses a plain river gatekeeper group intelligent regulation and control method based on deep reinforcement learning. The method comprises the steps that a flow prediction module, a knowledge constraint reinforcement learning module combining domain knowledge and an experience library and a multi-agent reinforcement learning module are built; integrating the calibrated knowledge constraint reinforcement learning module with a flow prediction module and a multi-agent reinforcement learning module to form a complete plain river network intelligent regulation and control model; and on the basis of the calibrated plain river network intelligent regulation and control model, future meteorological and hydrological prediction data are accessed, and a reasonable gate group scheduling scheme is rapidly output. The method has the advantages that the scheduling scheme cluster is intelligently generated, the complex and dynamically changing plain river gatekeeper group scheduling problem is efficiently solved, the flexibility, timeliness and intelligent level of a gatekeeper group scheduling system are improved, the possible'non-realistic solution 'problem of a machine learning method in practical application is solved, the interpretability of a result and the practical operability of a scheme are enhanced, and the method is suitable for large-scale popularization and application. And a self-adaptive closed loop of model-environment-feedback-regulation and control correction is formed.

The invention discloses a watershed multi-scale water quality target overall regulation and control method, which is characterized in that a regulation and control unit is delimited based on DEM data, a multi-scale spatial analysis framework is constructed through a hydrological topology and watershed nesting relationship, and the coupling process analysis of pollutant discharge, absorption and transportation is realized; deducing a water quality control target of each catchment area step by step through a process coupling reverse algorithm by taking a watershed outlet section water quality assessment target as a reference; dynamically quantifying a balance relation among pollutant discharge, land area system interception and river network system absorption based on a source-sink profit and loss intensity index, and realizing source-sink state diagnosis of different regulation and control units; and finally, generating a basin overall regulation and control scheme by delimiting a pollution emission hot spot area and a weak absorption capacity area and analyzing a regulation and control scene. The method can be widely applied to water environment refined treatment scenes such as river and lake water quality target management and pollution control zoning, and provides scientific basis and technical support for watershed water environment protection and overall restoration.

The invention discloses a method for efficiently and finely evaluating nitrogen and phosphorus consumption of a polder small micro water body, and relates to the technical field of water environment health evaluation. The method comprises the following steps: extracting pools, ditches, river network systems and hydraulic structures in a polder; based on a digital elevation model, rainfall and water level data of a polder, a geographic information system is utilized to analyze natural confluence, extreme conditions and a ground surface confluence process under strong manual regulation and control, a source-flow-confluence path of a small and micro water body in the polder is defined, and a small and micro water body topology network structure of the polder is formed; constructing a coupling source land nitrogen and phosphorus load accounting algorithm, an aquatic vegetation interception algorithm and a biogeochemical cycle process accounting algorithm, and depicting the nitrogen and phosphorus load current situation and the absorption potential of a single small and micro water body; and constructing a polder small and micro water body topology network structure-based nitrogen and phosphorus consumption network flow algorithm to realize small and micro water body nitrogen and phosphorus consumption load amount and retention amount accounting so as to complete polder small and micro water body nitrogen and phosphorus consumption evaluation.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention provides a method for predicting medium and long-term inflow runoff of a large lake in a river network area. The method comprises the following steps: collecting data and dividing branches into low, medium and high influence branches; a SWAT model and a physically constrained LSTM model are adopted to predict low and medium influence branch runoffs respectively; screening runoff prediction results of low and medium influence branches related to the high influence branches as prediction factors, and constructing a DBN-BP model to realize Euclidean prediction of the runoff of the high influence branches; and constructing runoff probability distribution by adopting a conditional normalization flow model, and realizing error correction through a nonparametric residual model. According to the invention, for river network regional differentiation modeling, physical constraint and data driving methods are combined, the problems of human activity interference and uncontrolled interval runoff prediction are solved, the prediction precision is effectively improved, and decision support is provided for water resource management.

The invention relates to the field of water resource analysis models, discloses a drainage basin scale phosphorus two-stage output coefficient model construction method, and aims to accurately calculate drainage basin phosphorus output load. The method comprises the following steps: firstly, obtaining watershed related data including a digital elevation model (DEM), a river network system, phosphorus load, soil, meteorology, hydrology and total phosphorus concentration data of river water; secondly, calculating a sub-basin phosphorus output coefficient SPOC and a river channel phosphorus output coefficient RPOC, and evaluating phosphorus load transmission and attenuation conditions; then, constructing a regression model, and improving the accuracy of the model through regression analysis of factors such as rainfall, terrain gradient and river network density; then, calculating a basin total phosphorus output load based on a regression model, and analyzing a spatial-temporal change trend of the basin total phosphorus output load; finally, the model is verified and optimized through measured data, model parameters are adjusted through regression analysis and cross validation, and the prediction reliability is improved. The method can effectively evaluate the phosphorus output load change of the drainage basin, and provides support for water quality management.