662 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 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 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 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 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 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 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.

The invention discloses a river nitrogen pollutant source analysis method based on drainage basin nitrogen turnover and isotope, and particularly relates to the technical field of water treatment.The river nitrogen pollutant source analysis method comprises the following steps that a drainage basin to be researched is selected, river water and soil samples in the drainage basin are systematically collected, and physical and chemical indexes and isotope indexes # imgabs0 # of the collected samples are tested. Nitrate marked by nitrogen isotope delta 15N / delta 18O and # imgabs1 # are used as key indication marks to perform high-precision identification on nitrogen sources in a river and an environmental system, the contribution proportion of different nitrogen sources to the nitrate concentration in a water body is quantified through a stable isotope analysis model, and the concentration of the nitrate in the water body is calculated. And in combination with data, the rates of denitrification and anaerobic ammonia oxidation in environmental samples, sediments and soil are directly measured through combined application of an isotopic tracer technology and a membrane inlet mass spectrometer technology.

The invention discloses an RCBAM model construction method for remote sensing inversion of river flow. The method comprises the following steps: 1) collecting remote sensing image data; (2) high-precision DEM production; 3) obtaining a central line of a river channel; 4) river width and gradient acquisition; 5) river section shape extraction; 6) calculating an open-current area; and 7) estimating the river flow. According to the method, the section shape of the river channel is obtained based on a high-precision digital elevation model (DEM), and the section area of the river channel is dynamically calculated by combining instantaneous water surface information obtained by a remote sensing image, so that the problem that defects exist due to the fact that the overflowing area is obtained by depending on an empirical formula in a traditional BAM flow model is effectively solved, and river flow estimation considering the shape characteristics of the river channel is realized. And efficient and accurate river flow remote sensing inversion can be realized.

The invention discloses a river surface flow velocity measurement method and system based on physical constraint and multi-modal fusion, and relates to the technical field of multi-modal data processing, a target area is determined, and river surface image sequence data, depth information and environment data of the target area are acquired; preprocessing the collected data, embedding redundant codes in data representation, and synthesizing to form a basic data set; and based on the basic data set, performing preliminary optical flow estimation on the river surface image sequence data by using an optical flow method. According to the method, optical flow information, depth information and environment data are fused, comprehensive utilization of multi-source data is achieved, in the data fusion process, redundant codes in data representation are combined for error detection and correction, error points in the data are effectively recognized and corrected, the reliability and accuracy of the data are improved, and the data fusion efficiency is improved. And by utilizing a multi-modal data fusion and error correction mechanism, the robustness of the system is remarkably enhanced, so that the system can stably operate in a complex and changeable river environment.

The invention discloses a river water quality prediction method fusing quantum-like features and a graph-time sequence model. The river water quality prediction method comprises the following steps: acquiring multivariable water quality observation data, time sequence meteorological data and static basin attribute data of a plurality of river sites; preprocessing the data; generating a meteorological quantum feature vector and a static basin attribute quantum feature vector; after the meteorological quantum feature vector and the static watershed attribute quantum feature vector are spliced and fused with the spatial dependence features, inputting the spliced and fused data and the preprocessed multivariable water quality observation data into a graph-time sequence model for training; and performing reverse normalization processing on simulation output obtained by calculation of the trained graph-time sequence model to obtain continuous day-by-day river water quality simulation data, and evaluating model performance to predict day-by-day continuous river water quality data. According to the method, the corresponding space-time class quantum feature vectors are generated by executing high-dimensional class quantum feature transformation on the multi-source heterogeneous data, so that the characterization capability of the model on the nonlinear interaction relationship of the space-time features is enhanced.

The invention discloses a river spill flood routing rapid simulation method based on river adaptive segmentation and strong water balance constraint, and the method comprises the steps: collecting and processing basic data of a drainage basin, building a rapid calculation model of river spill flood inundation, and carrying out the rapid prediction and risk analysis of the river spill flood. Rapid and fine simulation of watershed river overflow flood is realized, four-pre requirements of watershed flood control fine simulation, real-time early warning and forecasting, rapid scene deduction and the like are met, and the method can also serve watershed flood control planning and design.

The invention discloses a river pollution detection method based on multi-modal data fusion, and belongs to the technical field of environment monitoring. The method comprises the following steps: firstly, collecting water quality sensor data, remote sensing image data and meteorological data, and preprocessing the data, including data cleaning, normalization and feature extraction; feature fusion is carried out on the preprocessed data, comprehensive features capable of representing the river pollution condition are extracted, and dimension reduction is carried out on the fused comprehensive features; in the model training and optimizing stage, a river pollution detection model based on fusion features is constructed, and the feature extraction capability and the calculation efficiency of the model are enhanced; training and optimizing the model by utilizing an optimization algorithm, and adjusting parameters and a structure of the model; finally, the optimized model is applied to actual river pollution detection, collected data are analyzed and processed in real time, river pollution events are detected, early warning is given out in time, and high-precision river pollution detection is achieved.

The invention discloses a lightweight optical flow neural network method for river velocity measurement, and the method comprises the steps: collecting a dynamic monitoring image of a water area surface flow field, calculating an approximate optical flow field through combining with a DeepFlow optical flow algorithm, and generating an optical flow label; an improved lightweight ConvFFN-Flow optical flow estimation model is built, and the model comprises a feature extraction module, a 4D related structure body module, a GRU optical flow residual error iteration module and an up-sampling module; by introducing a ConvFFN module, the model parameter scale and the calculation amount are reduced, and the optical flow prediction precision is kept; and based on pixel coordinates of front and rear frames of images of the optical flow field data, obtaining corresponding positions in a real three-dimensional space, calculating an actual displacement amount, and deducing to obtain a real flow velocity vector size and a flow direction of the water body surface. The method still has stable and high-precision optical flow estimation capability when being used for processing scenes such as fine river water surface texture and complex flow state change, and meanwhile, the calculation overhead and the storage demand are remarkably reduced.

The invention relates to the technical field of water quality prediction, in particular to a river water quality prediction method and system based on a machine learning coupling hydrological model, and the method comprises the following steps: obtaining a data set, and dividing the data set into a training set and a test set; constructing a limit gradient lifting model, and performing hyper-parameter optimization on the model; performing correlation analysis on the water quality parameter set and the water quality index WQI value based on the trained limit gradient lifting model, and screening to obtain key water quality parameters influencing the water quality index WQI value; constructing an LSTM model and a soil and water evaluation tool model; simulating a future hydrological water quality process based on the soil and water evaluation tool model, and calculating and outputting a future water quality parameter result; and inputting a future water quality parameter result into the trained LSTM-WQI model to predict a future river WQI value so as to obtain a prediction result. According to the invention, the machine learning algorithm is coupled with the hydrological model to construct the water quality evaluation model which is convenient to use and adapts to local conditions, and efficient and accurate prediction of future water quality is realized.

The invention relates to the technical field of data monitoring, in particular to a river hydrological data monitoring method and system based on a water conservancy project. The severity and the state reference time period of the current moment are obtained according to the difference characteristics of the flow velocity at the current moment and the reference flow velocity and the difference characteristics of the rainfall and the reference rainfall; obtaining a first hydrological feature weight and a second hydrological feature weight according to the data features of different hydrological data in the state reference time period; and according to the first hydrological feature weight and the second hydrological feature weight, obtaining a fusion feature value and a fusion feature sequence of the hydrological data of all the positions. According to the fusion feature sequence and the severity, the dynamic threshold value of the current moment is obtained through different preset prediction models; obtaining a final dynamic threshold through a neural network according to the dynamic threshold, the fusion feature sequence and the severity; hydrological monitoring is carried out according to the fusion feature value and the final dynamic threshold value, and the accuracy of river monitoring and early warning is improved.

The invention provides a reservoir area landslide susceptibility evaluation method considering reservoir water level change factors. The method comprises the following steps: preparing data; constructing a model; introducing an adjustment coefficient; probability calculation and loss function definition; optimizing parameters; carrying out regularization processing; and outputting a result. The invention mainly considers the influence of reservoir water level change on reservoir area landslide susceptibility evaluation, provides a reservoir area landslide dynamic susceptibility evaluation method considering reservoir water level change, and mainly solves the following problems: a traditional landslide susceptibility evaluation technology is mainly oriented to administrative division, and the method mainly aims at evaluating the reservoir area landslide susceptibility, so that the reservoir area landslide dynamic susceptibility evaluation method can be used for evaluating the reservoir area landslide susceptibility. A river distance is introduced as an important influence factor of reservoir area landslide susceptibility evaluation.

The invention provides a river turbidity partition prediction method and device and a server, and relates to the technical field of water quality monitoring, and the method comprises the steps: obtaining a training data set through a water quality monitoring buoy, carrying out the data analysis processing of the training data set through a preset distance analysis model, and determining the comprehensive distance between all measurement points in a monitoring water area; carrying out clustering analysis processing on the training data set by utilizing a density-based spatial clustering algorithm and based on the comprehensive distance so as to divide the monitored water area into a sub-area set, and by calculating the closeness of each sub-area, determining the sub-area with the closeness greater than a preset closeness threshold value as a key region of interest; and determining a target influence factor influencing the turbidity through a random forest algorithm, determining a target turbidity prediction model based on the target influence factor through a graph convolutional network, and predicting the river turbidity by using the target turbidity prediction model to obtain a river turbidity prediction result. According to the method, the river turbidity prediction accuracy can be remarkably improved.

The invention relates to the technical field of data processing, in particular to a river pollution monitoring management method fused with environmental information perception. According to the method, monitoring points are classified according to information collected in a non-rainfall period, and a plurality of monitoring point clusters are obtained. In the target rainfall period, the rainfall influence degree of the monitoring points is determined by analyzing pollutant concentration data changes before and after analysis and rainfall information, and then the target monitoring points needing data correction are screened out. A time correction method is adopted, reference time periods are selected in the non-rainfall time periods, the reference weight of each reference time period is determined by analyzing the time sequence position of the reference time periods and the rainfall influence degree of the latest rainfall time period, and then time correction is conducted; and further performing space correction by using other monitoring points in the monitoring point cluster obtained by classification to finally obtain corrected pollutant concentration data. According to the method, the influence of rainfall on pollutant concentration data is eliminated, and the data reference is increased while the data coherence is ensured.

The invention discloses a device for acoustically monitoring suspended sediment concentration and section particle size mean value of an ocean and a river, and relates to the technical field of ocean and river water environment monitoring. The four groups of piezoelectric ceramic transducers at the transmitting end are used for transmitting sound pulses with multiple frequencies into the same water body, and the broadband hydrophone array at the receiving end is used for synchronously receiving acoustic backward scattering signals which are scattered back. Wavelet threshold denoising and frequency domain filtering are combined, acoustic back scattering signals are preprocessed, turbulence and biological noise are eliminated, nonlinear errors caused by the particle size-concentration coupling effect are solved through an inversion algorithm fusing attenuation and scattering signals and in combination with a dynamic hierarchical data processing technology, and the accuracy of the acoustic back scattering signals is improved. The device realizes high-precision real-time measurement of suspended sediment parameters in ocean and river environments, has the capability of adaptive correction of environmental parameters, and is suitable for scientific research and engineering monitoring scenes.

The invention relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on the digital twinning technology, and the method comprises the steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, obtaining the reflection intensity and elevation data of each point cloud, clustering all point clouds corresponding to the ground, and obtaining a three-dimensional model; recording the areas where all point clouds corresponding to the ground in each cluster are located as local areas; and calculating a coplanar characterization value, an erosion evaluation value, an instability risk value, a soil fixation evaluation value and a terrain evolution factor of each local area, and constructing a digital twinborn simulation platform of the river basin terrain in combination with the three-dimensional terrain model to perform analog simulation on flood disasters. The defect that a static terrain model cannot respond to terrain evolution caused by flood disasters can be avoided, simulation errors generated by a digital twin simulation platform in the flood disaster evolution process are effectively reduced, and the accuracy of risk assessment of the flood disasters is improved.

The invention discloses a surface water storage amount monitoring method and system based on space-air-ground water multi-source collaboration, and relates to the technical field of water resource investigation, and the method comprises the steps: obtaining multi-source collaboration collection data of a target water body, and generating a multi-source data set of a unified space-time reference; carrying out automatic extraction and change detection on a surface water area range by using a water body index method and deep learning to obtain a water area boundary; taking a water area boundary as a spatial constraint, extracting ashore terrain and underwater terrain data, and inputting the ashore terrain and underwater terrain data into a deep learning fusion model based on an encoder-decoder structure to generate a water-land integrated digital elevation model; and according to the water body types, based on the model, respectively adopting corresponding deep learning models to carry out storage volume monitoring, and calculating the water storage volumes and dynamic changes of different water body types. Efficient fusion and intelligent processing of multi-source data are achieved, the precision and efficiency of surface water storage capacity monitoring are remarkably improved, and the method is suitable for refined dynamic monitoring of various water bodies such as rivers, reservoirs and ponds.

The invention provides an in-situ curing and repairing method for heavy metals in river sediment. The in-situ curing and repairing method comprises the following steps: S1, detecting the content and physicochemical properties of the heavy metals in the river sediment; s2, designing a repair material formula; s3, preparing and putting a repairing material; s4, mixing and stirring the bottom mud and the repairing material; s5, monitoring and evaluating the repairing effect; and S6, repairing effect feedback and adjustment. According to the method, multiple linear regression, curved surface fitting, self-organizing mapping and mixed density network algorithms are fused, the sediment heavy metal content and physicochemical property data are fully utilized, a repair material formula can be accurately designed, and compared with a traditional single algorithm or empirical formula design, the repair material proportion can better fit the actual pollution condition, and the repair efficiency is improved. The utilization rate of repair materials is improved.

The invention discloses a river flow remote sensing inversion method based on a local water area, and belongs to the technical field of environment remote sensing and hydrological observation, and the method comprises the following steps: S1, obtaining a flow monitoring section position, an actually measured flow and a remote sensing image of a region of a target river; s2, determining a plurality of research areas; s3, converting the vector file and performing cloud removal processing; s4, generating a binary water surface distribution diagram; s5, obtaining historical maximum riverway boundary vector data; s6, constructing a water surface area data set; s7, forming a paired sample set; s8, constructing a river section flow inversion model, and performing training and verification; s9, outputting a river flow inversion result based on the river section flow inversion model; only the local water area of the river channel needs to be monitored, the monitoring cost is reduced, meanwhile, the dependence of a traditional remote sensing technology on the width of a large river can be broken through, the small and medium-sized river channel flow inversion problem is effectively solved, and the applicability of the remote sensing means in narrow river sections and complex terrain areas is remarkably expanded.