1212 results about "Fluvial" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention discloses a river model multi-data processing analysis method and system, and relates to the technical field of hydraulic engineering, and the method comprises the steps: extracting a motion feature vector from a digital twin model, and carrying out the preprocessing to generate a standard river motion data set; according to the method, the sediment space distribution concentration Nsc (x, y, z, t) in a three-dimensional coordinate grid is calculated through a standardized data set, and the change trend of the sediment space distribution concentration Nsc (x, y, z, t) along with time is evaluated. When the sediment space distribution concentration Nsc (x, y, z, t) is larger than 0, highlighting marking is conducted on the abnormal area through a visual interface; when the sediment space distribution concentration Nsc (x, y, z, t) is less than or equal to 0, the sediment concentration is normal. For an abnormal area, the method further calculates the sediment conveying capacity Qs and the sediment deposition rate S, accurately predicts the moving path, the sediment deposition area and the sediment deposition speed of the sediment, dynamically analyzes the sediment movement condition of the river, and can track the sediment change trend in real time in a complex dynamic environment.

The invention relates to the technical field of coupling simulation, in particular to a river and underground water coupling simulation parameter generation method and system. The method comprises the following steps: collecting remote sensing and ground sensing monitoring data, and generating a multi-source space-time initial data set through time mark calibration, space resampling and signal-to-noise ratio weighted fusion; extracting features between the water level of the river and the water level of the underground water based on the data set, constructing a river-underground water space-time topological structure and identifying an interaction mode, and forming a space-time coupling feature map through significance screening and periodic enhancement; performing parameter inversion by adopting a hydrodynamic equation, and generating a physical inversion parameter set in combination with sensitivity analysis and local optimization; a parallelization hydrological simulation framework is constructed, error-driven optimization is executed, and dynamic optimization parameters are obtained; river and underground water exchange simulation is executed, and finally, coupling simulation parameter set updating is achieved through sliding window deviation evaluation and incremental parameter correction. Therefore, the precision and operability of a coupling simulation result are improved.

The invention discloses a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode. The method comprises the following steps: acquiring a river network database and gridded hydrotopographic data, and interpolating the gridded hydrotopographic data to a refined confluence grid with a target resolution to obtain interpolated elevation data; according to the altitude data, the flow direction data and the cumulative flow after interpolation, determining vector data of a river network center line and a water collection area boundary, segmenting the river network according to the Strahler river level, and determining river reach of different Strahler levels; inputting the vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network confluence model, and simulating by adopting different Manning coefficients for river reaches with different Strahler levels to obtain simulated flow; and through a dynamic dimension search algorithm, the Manning coefficients of the river reaches with different Strahler levels are optimized respectively, so that the objective function is minimum.

The invention discloses a pollution research and water quality prediction method based on river segmented water environment capacity analysis. The method comprises the following steps: accurately dividing a drainage basin into water environment capacity accounting units; obtaining a river water body image through satellite remote sensing; identifying and quantifying the positions and the intensities of point source and non-point source pollution sources in the drainage basin by utilizing the water quality parameters of the river obtained by the remote sensing image; establishing a river water quality prediction model; calculating the water environment capacity of each river reach under different surface water environment quality standards; comparing the estimated pollutant discharge amount with the water environment capacity under each water quality standard, and judging whether the pollutant discharge amount exceeds the capacity or not; and predicting future water quality change by using the model. According to the method, remote sensing, GIS and machine learning technologies are utilized, rapid analysis of the river segmented water environment capacity and accurate prediction of the water quality are achieved, dependence on a large amount of field sampling and complex software simulation is reduced, and the working efficiency is greatly improved.

The invention discloses a river pollution tracing method and system based on underground water quality pollution monitoring, and relates to the technical field of river pollution tracing. According to the river pollution tracing method based on underground water quality pollution monitoring, the real-time concentration value of each pollutant in underground water is obtained in real time and analyzed to obtain the underground water pollution real-time evaluation index; according to the method, a plurality of abnormal measurement points with abnormal concentration in a set area are identified, and the traceability position coordinate of each pollutant with abnormal concentration in underground water in a river is analyzed, so that the real-time evaluation index of underground water pollution can be continuously calculated by acquiring the pollutant concentration data of each measurement point in real time; and whether the pollutant concentration exceeds the preset range or not is judged in time, so that the defects of hysteresis and untimely data updating of traditional water quality monitoring are overcome, dynamic monitoring of the pollutant concentration can be realized, and quick response when the pollutant is abnormally increased is ensured.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention relates to the technical field of optical line protection, and discloses a river water quality monitoring method based on an unmanned aerial vehicle hyperspectral remote sensing image and machine learning, which combines the unmanned aerial vehicle hyperspectral remote sensing image with a machine learning algorithm to construct a river water quality parameter inversion model. Comprising the following steps: collecting a water sample, obtaining an unmanned aerial vehicle hyperspectral image and preprocessing; extracting an image of the river part and selecting the reflectivity of each wave band of the image; performing spectrum transformation on the original spectrum of the image, establishing an inversion data set through feature screening, and constructing a water quality parameter inversion model by using a machine learning algorithm; and simulating the water quality parameters of the river through the water quality parameter inversion model, and visualizing the simulation result. According to the method, urban inland small river water quality monitoring can be rapidly achieved at low cost, the dual problem that the time resolution and the space resolution are limited in urban small river monitoring based on satellite remote sensing images is solved, and a scientific basis is provided for urban river intelligent inversion.

The invention relates to the technical field of ecological restoration prevention and control, in particular to a sea-entering river pollution source targeted blocking and ecological restoration integrated prevention and control system which comprises a pollution source recognition unit, a targeted blocking unit, an ecological restoration unit and a cooperative control platform. The recognition unit combines a multispectral unmanned aerial vehicle with a water quality sensor and positions a pollution source through artificial intelligence traceability; the targeted blocking unit implements physical-chemical combined blocking according to pollution types; the ecological restoration unit purifies water and reconstructs ecology through a modular constructed wetland and the like; the cooperative control platform optimizes each unit strategy based on hydrological data; each unit correlation formula accurately calculates parameters, such as a recognition algorithm, a gate dam opening control formula and the like. According to the invention, multi-technology fusion and system collaboration are realized, pollution events can be quickly responded, measures are accurately applied, the sea-entering river pollution prevention and control efficiency is effectively improved, the treatment cost is reduced, and drainage basin water environment ecological restoration and sustainable development are promoted.

The invention discloses a method for evaluating the ecological restoration effect of a micro-polluted river, particularly relates to the field of ecological restoration, and is used for solving the problem of accurate identification and evaluation of sudden pulse pollution events of the micro-polluted river. The data consistency and high resolution are ensured through a unified time and space reference axis, and the accuracy of event identification is guaranteed; a time sequence windowing comparison and deviation mapping inspection technology is adopted, random interference is effectively filtered, and an original fragment of a pulse pollution event is extracted; the impact force and diffusion characteristics of the pollution event are analyzed by applying an amplitude change and propagation dynamic quantification method, and accurate data support is provided for a risk distribution map; according to the method, a correlation mapping model of adaptive clustering and priority evaluation is combined, pollution events are scientifically classified and sorted, high-risk areas with high impact force and high diffusion potential are accurately identified, accurate calibration and deployment and control division are carried out according to a risk distribution map, and the pertinence and efficiency of treatment measures are optimized.

The invention relates to the technical field of water resource scheduling, and provides an urban inland river system intelligent resource scheduling management system and method based on dynamic data collaboration, and the method comprises the following steps: S1, collecting river hydrological data, water quality indexes, urban water demands and water transfer facility state data in real time, and carrying out the time-space alignment and abnormal value filtering; s2, a hydrodynamic force-water quality coupling model of multiple water sources is established, a multi-objective optimization model is established in combination with constraint conditions of ecological base flow and an engineering upper limit, and the multiple water sources include but not limited to river water, underground water, reservoir water, reclaimed water, rainwater and desalinated seawater; and S3, carrying out global dynamic optimization on the water transfer proportion and the water transfer facility parameters by adopting a model algorithm, and minimizing the water transfer cost and the ecological influence. By quantifying the maximum flow upper limit and the ecological base flow threshold value of each water source and optimizing the water transfer proportion and facility parameters, the water resource utilization efficiency is improved, and it is guaranteed that the water transfer cost and the ecological influence are balanced in the urban river water dispatching process.

The invention discloses a drainage basin flood risk assessment method and system based on a particle recognition algorithm, and belongs to the technical field of flood early warning. Although the real-time flow of a river can be measured through an existing scheme, how to deduce and evaluate the flood inundation condition is not involved, and consequently the flood inundation condition around a river bank cannot be rapidly known. According to the drainage basin flood risk assessment method based on the particle recognition algorithm, the drainage basin spatial discrete model, the flow velocity calculation model, the flow simulation model, the two-dimensional hydrodynamic model and the flood risk assessment model are constructed, so that the section flow of the drainage basin can be calculated, and the flood inundation condition can be deduced and assessed; therefore, the flood inundation risk and inundation data around the water area can be quickly known, the requirement of flood risk analysis of the drainage basin can be met, the accuracy of flood disaster early warning is improved, and more data supplementation and management bases are provided for flood control of the drainage basin.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention relates to the technical field of mountainous river fish habitat habitat restoration, and relates to a mountainous river fish habitat restoration method based on numerical simulation optimization, comprising: acquiring habitat data of a mountainous river fish habitat, and performing preliminary ecological restoration measures on the mountainous river fish habitat; constructing a numerical simulation model to predict the erosion and deposition evolution result of the fish habitat in the mountain river after the preliminary ecological restoration in a set period, and identifying an area of which the erosion and deposition evolution degree meets a preset threshold value in the set period; and carrying out riverbed substrate transformation on the identified area based on the target fish, evaluating the habitat improvement effect under the combined action of the preliminary ecological restoration measure and the riverbed substrate transformation through a habitat suitability model, and completing the restoration of the fish habitat in the mountain river based on the evaluation result. According to the method, dredging, spur dike and substrate transformation are combined, and numerical simulation optimization is combined, so that the effectiveness, continuity and scientificity of restoration of the fish habitat in the mountain river are improved.

The invention discloses a river water level dynamic monitoring and flood overflow risk prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source hydrological data, and constructing a time series data set; s2, performing interpolation, denoising and normalization processing on the data to generate a unified time sequence format; s3, constructing a water level prediction model comprising a bidirectional long short-term memory network and an attention mechanism; s4, inputting the preprocessed data into the water level prediction model, and outputting a multi-time-step predicted water level sequence; s5, a dynamic threshold value is set according to the historical extreme value and the real-time hydrological condition, and the flood overflow risk is judged; s6, generating and caching a risk tag, and recording an error; s7, outputting a prediction result and risk information through a communication interface; and S8, periodically updating the input data in a rolling manner, and repeatedly executing the prediction and monitoring process. According to the invention, depth prediction and a dynamic threshold control mechanism are fused, and water level monitoring and flood overflow risk intelligent early warning are realized.

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 river water ecological health monitoring method and a river water ecological health monitoring system, relates to the technical field of ecological health monitoring, and aims to solve the problem that a traditional water quality monitoring method mainly depends on water sample collection and laboratory analysis, and judges the water quality by measuring indexes such as dissolved oxygen, pH value, ammonia nitrogen, total phosphorus, heavy metal and dissolved organic matters in water; the river runoff is calculated from the precipitation and the drainage basin area, and the monitoring points are set in combination with the environmental information, so that an accurate data basis can be provided for subsequent water quality health assessment. The water quality condition is predicted by utilizing the water flow speed and the pollution point discharge amount and then compared with an actual monitoring result, so that potential problems can be found in time and a monitoring strategy can be optimized.

To have a high nature imitation effect of reference point micro inhabitation environment, hold water ecological space heterogeneity of a target water body at the maximum without changing a microorganism / organic feature in a natural water ecological system in the whole construction process, and realize a natural water ecological environmental status.SOLUTION: A selection and construction method of a reference point in interfered submergence type river water ecology includes steps of: dividing a basin basic space unit S1; analyzing an influence on an ecological environment stress of human activity in the basin basic space unit and extracting a water ecological feature S2; selecting a reference candidate point S3; evaluating an aquatic organism status of the reference candidate point S4; constructing a nature imitation reference point S5; and evaluating the aquatic organism status of the nature imitation reference point S6.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of water quality prediction, and particularly relates to a river basin water quality prediction and early warning method and device based on a multi-model processing technology and a medium, and the method comprises the steps: firstly obtaining and preprocessing historical water environment monitoring data and real-time water environment monitoring data of a river basin; then constructing a water quality prediction deep learning model, and outputting a river basin water quality prediction result after training verification; constructing a water quality index prediction mechanism model, and after training verification, outputting water quality index prediction results of the river basin according to water quality prediction results; and finally, performing threshold judgment based on the river basin water quality prediction result and the dynamic change result of each water quality index of the river basin, and calling an expert emergency early warning plan according to the judgment result. According to the invention, the problem of poor prediction effect in the prior art can be solved.

The invention discloses a quantitative source tracing method for river and lake water pollution in combination with a knowledge graph and machine learning. The invention relates to the technical field of water quality pollution traceability, and the method comprises the steps: collecting and processing target river section data, constructing a hydrodynamic force-water quality model, and obtaining river downstream pollutant diffusion characteristics and target section concentration time sequence characteristics under different discharge situations of each discharge port based on the discharge port position of a target river section through a unit pulse response test; the method comprises the following steps: constructing a source intensity-time-concentration relation knowledge graph, randomly extracting a sample set from the graph, training the sample set by adopting a machine learning method, learning a nonlinear mapping relation between a downstream section concentration time sequence and multi-outlet source intensity, carrying out dynamic inversion of a river pollutant diffusion process, and realizing rapid positioning and contribution quantification of a pollution source. And finally, generating probability distribution of pollution source positions by adopting a Monte Carlo sampling method to assist in improving the traceability judgment priority, thereby improving the accuracy and response speed of river water quality pollution traceability.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

The invention relates to a high-dimensional statistics and hydrodynamic force coupled flood risk assessment method and system. The method comprises the following steps: acquiring historical observation data covering river flow, tide level and rainfall events; constructing a univariate marginal distribution function; connecting the univariate marginal distribution functions of the river flow, the tide level and the rainfall event, and simulating a joint probability distribution function; coupling the joint probability distribution function with the mechanism model to obtain a probability-mechanism coupling model; inputting the determined joint probability boundary condition into a probability-mechanism coupling model, simulating to obtain composite flood scenes in different return periods, and comparing the composite flood scenes with single variable flood scenes in different return periods; and quantizing the contribution of each variable to the change of the submerging area by using the obtained submerging area and the corresponding rainfall event, river flow and coastal tide level data. According to the method, the defect that traditional single-factor evaluation cannot capture multi-factor interaction is overcome, and the accuracy of flood disaster evaluation is greatly improved.

The invention discloses a river water pollution real-time tracing method, which comprises the following steps: obtaining a pollution source position of a river basin, obtaining a pollutant type discharged by a pollution source, and obtaining pollutant particle parameters and pollutant types; obtaining a river connection relation of the river basin, and confirming an input flow and an output flow of pollutants; based on the output flow of the pollutants, the pollutant type of the river basin is obtained, and the input flow of the pollutants is obtained; obtaining a theoretical pollutant level based on the theoretical water flow velocity and pollutant particle parameters of the river basin; based on the actually measured pollutant concentration and the actually measured water flow velocity, acquiring an actually measured pollutant level; and obtaining the pollutant source based on the actually measured pollutant level and the theoretical pollutant level. The problems of difficulty in real-time detection and insufficient traceability precision in a water pollution analysis method based on sampling in the prior art are solved. The traceability speed and precision are fully improved, and various hydrological conditions in rivers can be handled.

The invention discloses a river sediment scouring and silting simulation method, and belongs to the technical field of hydraulic engineering and hydrological simulation. In order to solve the problem that it is difficult to accurately simulate the river sediment erosion and deposition evolution trend in the prior art, the method comprises the steps that on the basis of actually measured topographic data and channel chart data of a target river reach, a water-sediment coupling mathematical model is constructed by adopting a non-uniform sediment unbalance sediment transportation theory, and the model interacts with a water power module and a wave module in real time through an MCT coupler; actually measuring a flood and water and sediment series data calibration verification model; and obtaining future water and sediment process data predicted by a plurality of climate modes, inputting the future water and sediment process data into the verified model, dividing river reaches according to the characteristics such as riverbed gradient and river channel curvature, and calculating the erosion and deposition amount of each sub-river reach in the future 50 years. The method can simulate water flow and sediment movement of rivers, reservoirs and lakes, can be applied to the fields of riverway regulation planning, flood control and disaster reduction decision making, reasonable development and utilization of water resources and the like, and provides important data support and scientific basis for construction and management of water conservancy projects.