132 results about "Rainfall runoff" patented technology

The invention relates to the technical field of urban drainage and waterlogging prevention, in particular to an urban real-time drainage scheduling method and system based on deep reinforcement learning. The method comprises the steps of constructing a dual-target reward function and constraint conditions for minimizing the total amount of surface waterlogging and the overflow frequency of a combined system discharge port, performing rainfall runoff simulation by adopting a hydrodynamic mechanism model and combining basic data of a target area, and establishing a multi-agent model based on deep reinforcement learning. The method comprises the following steps: constructing interactive coupling of decision actions and simulated environment states, obtaining updated environment states and drainage scheduling reward values through the coupling, driving a multi-agent model to generate a strategy network, predicting and generating drainage waterlogging prevention engineering actions based on real-time rainstorm information, and dynamically updating the actions by utilizing the periodically generated strategy network. According to the method, it is ensured that multi-objective optimization decision is rapidly achieved within response time, the waterlogging relieving effect is achieved, urban rainfall flood toughness is enhanced, and the real-time requirement is met.

The invention discloses a reservoir group delivery flow prediction method considering climate change and artificial regulation and storage. The method comprises the following steps: collecting hydro meteorological data of a research basin; determining an upstream inlet reservoir, an interval reservoir and an outlet reservoir according to the reservoir position distribution of the research basin; determining reservoir inlet flow calculation modes of the reservoirs at different positions and reservoir outlet flow models of the reservoirs; and for the outlet reservoir, according to the outlet flow of the upstream reservoir and the hydrometeorological data, the reservoir inlet flow of the reservoir is obtained through a vector autoregression model or a multiple linear regression model, the outlet flow simulation of the reservoir is carried out, and an outlet flow residual prediction model based on deep learning is established, so that the outlet flow prediction of the outlet reservoir is realized. According to the method, the influence of the artificial regulation and storage effect of the reservoir on the downstream reservoir is considered on the basis of the inflow and outflow hydraulic relation between the reservoir groups, and the outflow process of the downstream reservoir is predicted by combining the conceptual rainfall runoff model, the reservoir regulation model and the deep learning model.

The invention discloses a method for predicting the risk of a valley-type tailing pond being threatened by extra-small watershed flood, and the method comprises the steps: S1, obtaining the soil type, vegetation coverage and gradient of the extra-small watershed of the valley-type tailing pond, and the spatial distribution data of a valley, and building an underlying surface classification system; s2, non-linear key inducements in the rainfall-runoff process in the valley terrain extra-small watershed are extracted, dominant factors influencing runoff formation are recognized, and a non-linear confluence calculation numerical model is constructed; s3, constructing a distributed sensing monitoring network through a rainfall monitoring station and a runoff monitoring station; s4, constructing a layered three-dimensional hydrological hydrodynamic model of coupling rainfall runoff generation-slope confluence-shallow groundwater flow; and S5, establishing a risk assessment index system and a risk prediction model through a layered three-dimensional hydrological hydrodynamic model in combination with flood risk influence factors. According to the method, accurate prediction of the valley-type tailings pond under the flood threat in the extra-small watershed is realized, and the problem of poor predictability of the flood threat in the extra-small watershed is solved.

The present invention relates to urban rainfall runoff pollution prediction in urban water systems, and provides an urban rainfall runoff pollution prediction method based on integrated rolling decomposition method and deep learning algorithm. A rolling decomposition method is firstly used to decompose rainfall runoff sequence data into different sub-sequences; then decomposition is sequentially performed on added data, and future data is excluded, to prevent information leakage; a recurrent neural network is used to model and predict the sub-sequences; and finally, predicted results of the sub-sequences are summed to obtain the predicted result of rainfall runoff pollution.

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 discloses a drainage basin rainfall runoff prediction method and system based on a bidirectional long short-term memory network and multi-source data fusion, and relates to the technical field of drainage basin rainfall runoff prediction, and the method comprises the steps: collecting the multi-source data of a drainage basin, and carrying out the preprocessing; constructing a drainage basin rainfall runoff prediction model according to the preprocessed data; the model is trained, and a water balance constraint term is introduced into MSE loss for model optimization; performing model dynamic weight transfer learning of the data scarcity drainage basin, pre-training the model on similar drainage basin data, and obtaining an optimal drainage basin rainfall runoff prediction model through field adaptive fine adjustment of model parameters; and acquiring real-time data, and inputting the real-time data into the optimal drainage basin rainfall runoff prediction model to obtain a prediction result. Compared with one-way LSTM, errors in flood peak prediction are reduced through BiLSTM-ATT, it is guaranteed that a prediction result conforms to a hydrological law through a water balance constraint term, and cross-basin rapid deployment is supported through a transfer learning module.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention discloses a rainfall runoff simulation method for performing session flood data cleaning based on a dynamic threshold value. The rainfall runoff simulation method comprises the following steps: collecting actually measured rainfall and section flow data of a target drainage basin in a multi-year hour scale; performing flood event segmentation on the continuous rainfall runoff data sequence of the target drainage basin based on the flow hydrograph morphological characteristics, and extracting rainfall runoff data corresponding to each flood session; through preliminary rainfall screening, rainfall runoff total amount comparison screening and fine screening based on runoff coefficient truncation analysis, abnormal flood sessions with unreasonable magnitude relationships in the rainfall-runoff data are identified and eliminated; by screening key time points in the flood process, flood session data with an abnormal time sequence is eliminated; and taking the cleaned flood data as the input of the session hydrological model, and carrying out normal simulation. According to the method, the flood data cleaning process is standardized, unreasonable flood session data are eliminated in advance, input data are ensured to be clean, and thus the flood forecasting precision is improved.

The invention provides a small watershed non-point source pollution key source area determination method which can be applied to the technical field of pollutant analysis and the field of environmental science. The small watershed non-point source pollution key source area determination method comprises the following steps: acquiring multiple groups of rainfall runoff process information corresponding to a to-be-investigated geographic area; generating N groups of pollutant output quantity theoretical values based on the multiple groups of rainfall runoff process information and the N groups of land information; generating N groups of pollutant transmission resistance values based on pre-collected N groups of geographic information in one-to-one correspondence with the N geographic grid units; based on the N pollutant output quantity theoretical values and the N pollutant transmission resistance values, generating N pollutant output quantity actual values in one-to-one correspondence with the N geographic grid units; and on the basis of the N pollutant output quantity actual values, screening out at least one target geographic grid unit from the N geographic grid units to serve as a small watershed non-point source pollution key source area.

The invention provides a power grid equipment large-range progressive real-time flood forecasting method based on a three-water-source Xinanjiang model, which abandons a basin homogenization hypothesis of a traditional lumped model, divides a target basin into subunits with spatial uniqueness and topological connectivity through a Thiessen polygon method, and accurately matches underlying surface spatial differentiation features; according to the method, the limitation of single parameter calibration is broken through, an NSGA-I I multi-objective optimization algorithm is adopted to be combined with historical flood data, a parameter optimization system containing multiple indexes such as a flood peak flow error and a peak present time error is constructed, a Pareto optimal solution set is generated, a parameter knowledge base is constructed in a classified mode, and the problem of poor parameter adaptability is solved; a geographically weighted regression method is used for fusing satellite remote sensing, ground rainfall station and hydrometric station data, a dynamic correction mechanism is established, model input is updated every 30 minutes, and the rainfall runoff calculation temporal-spatial resolution is remarkably improved; after subunit outlet flow riverway convergence calculation is completed based on a Muskingum method, power grid equipment space distribution and vulnerability threshold values are innovatively associated.

The invention discloses a black soil erosion simulation system with rainfall runoff and snow melting confluence coupling effect. The system comprises a data acquisition system, a rainfall simulation system, a snow melting simulation system, a runoff collection and monitoring system, a black soil simulation area, a data processing and analysis system and a control system. The data acquisition module comprehensively collects various types of data of the black soil area; each simulation subsystem accurately simulates rainfall, snow melting and coupling processes; the runoff collecting and monitoring subsystem monitors runoff in real time; the black soil simulation area simulates the field condition; the data processing and analysis module deeply mines data, establishes a model and predicts a trend; and the control system realizes automatic accurate control. The whole system adopts a modular design and is convenient to maintain and upgrade. The operation, system flow, maintenance upgrading and application cases of each module are elaborated through a specific implementation mode, a powerful tool is provided for black soil erosion research, and a scientific basis is provided for making prevention and control measures.

The invention relates to the technical field of data processing, and discloses a rainfall runoff pollution early warning system and method.The system comprises a sample analysis module, a pollutant model construction module, a water quality prediction module, an early warning signal generation module and a pollution control module; carrying out physicochemical property analysis on the pavement sediment sample and the rainfall runoff sample of the service area to obtain pollutant concentration data and pollutant composition data; constructing a pollutant migration and transformation model based on dynamic changes of pollutants in the pollutant concentration data and the pollutant composition data in the runoff process; hydrology and hydrodynamic force of the service area are predicted based on the pollutant migration and transformation model, and runoff water quality change trends under different rainfall scenes are obtained; when the water quality prediction index in the flow water quality change trend exceeds a pollution early warning threshold value, a pollution early warning signal is generated; the pollution early warning signal is pushed to a management terminal, and a corresponding runoff pollution control measure is triggered; the rainfall runoff pollution early warning method can improve the accuracy of rainfall runoff pollution early warning.

A method and system for tracing the source of non-point source heavy metal pollution in a watershed, the method first determines the spatial distribution of soil heavy metal content under various land use types in the watershed, the sediment enrichment ratio and water solubility factor of soil heavy metals, and simulates the spatial and temporal distribution of rainfall runoff and soil erosion sand production, and then calculates the loss of land surface heavy metal pollution, and then combines the migration of heavy metal pollution in the river channel to calculate the loss of non-point source heavy metal pollution in the outflowing river section under various land use types, so as to obtain the contribution ratio of each land use type to the non-point source heavy metal pollution in the watershed, and finally determines the main source of the loss of heavy metal pollution in the watershed according to the contribution ratio. The present invention comprehensively considers the impact of land loss and river migration process on non-point source heavy metal pollution in the watershed, and can realize the accurate calculation of the loss load of non-point source heavy metal pollution at the watershed scale and the rapid and precise identification of pollution sources.

The invention relates to an urban non-point source pollution identification method and system based on rainfall events. The method comprises the following steps: step S100, decomposing rainfall data of a continuous time sequence into independent rainfall events and snowfall events; step S200, dividing grid units, and performing runoff calculation in each unit so as to obtain runoff information of spatial distribution; wherein the rainfall event runoff is estimated by adopting a runoff curve hydrological model method; calculating snow melting runoff by adopting a temperature index snow melting model; s300, estimating the average event concentration of the rainfall event by adopting an accumulation and scouring formula, and estimating the average event concentration of the snow melting event by using average observation values in different land utilization types; and step S400, realizing refined identification and accurate estimation of urban non-point source pollution load by integrating urban functional area characteristics, surface runoff simulation and event average concentration simulation of pollutants. The method is suitable for estimating the rainfall runoff pollution load in a large-scale region with relatively limited water quality monitoring data.

The invention provides a watershed flood early warning system and method based on hydrological model and mobile application coupling, and belongs to the field of watershed flood early warning. In the system, a communication and data transmission module collects data for hydrological model simulation in a watershed area; the model simulation module simulates hydrological data of rainfall runoff in a watershed area in real time by using a hydrological model based on the collected data, and transmits a simulation result to the flood risk forecasting module through the communication and data transmission module; the flood risk forecasting module generates a flood risk map based on the received simulation result and transmits the flood risk map to the communication and data transmission module; and the mobile application display module receives real-time early warning information generated by the communication and data transmission module based on the flood risk map, and displays the real-time early warning information to a user. The watershed flood is simulated in real time through the hydrological model, the flood forecasting and early warning information is sent to people in a watershed flood submerging range through the mobile application, and the accuracy and timeliness of watershed flood early warning can be improved.

The invention relates to the technical field of rainfall runoff simulation, and discloses a BIM-GIS (Building Information Modeling-Geographic Information System) coupling-based rainfall runoff dynamic simulation method, which comprises the following steps of: planning a simulation process based on input parameters and environmental conditions, determining required BIM data (including building geometric data) and GIS data (including terrain elevation data), and determining different scene types; then generating a dynamic simulation process set, determining an execution sequence, and generating a coupling parameter setting list and a data fusion scheme list of each scene; and finally associating the elements to generate an overall scheme. In addition, the invention also relates to the contents of dynamic simulation boundary determination, data subset extraction, scene type subdivision, simulation process updating, data fusion rule and database establishment and maintenance and the like. The method integrates the technical advantages of BIM and GIS, can adapt to different scenes, realizes data dynamic updating and efficient processing, improves the accuracy and reliability of rainwater runoff dynamic simulation, and can be used in the related field of urban rainwater management.

The invention provides a sediment concentration prediction method and system based on a freeze-thaw coupling physical constraint model, and relates to the technical field of sediment concentration prediction, and the method comprises the steps: obtaining hydrological data, meteorological data and various original data of soil and vegetation, and carrying out the preprocessing; constructing a benchmark sediment concentration physical model based on the multiple kinds of preprocessed original data, and constructing an input feature vector; inputting the input feature vector into a sediment concentration prediction ANN model, and outputting to obtain the sediment concentration; wherein in the training process of the sediment concentration prediction ANN model, reference sediment prediction and sediment mass conservation constraint of the physical model are introduced into an ANN model loss function, the loss function is enhanced by adopting a freeze-thaw weighting strategy, and the sediment concentration prediction ANN model in which the physical model and the ANN model are deeply fused is obtained. According to the invention, the stability and accuracy of prediction during extreme rainfall, runoff surge or frequent freezing and thawing are improved.

The invention discloses an automatic field rainfall runoff collecting device, and relates to the technical field of outdoor water resource collection, the automatic field rainfall runoff collecting device comprises a mounting cylinder, a guide transverse hopper and other structures, a main body structure of the automatic field rainfall runoff collecting device comprises the guide transverse hopper and the mounting cylinder, and a storage cylinder for storing and collecting a water source is inserted into the mounting cylinder; the guiding transverse hopper introduces field rainfall runoff into the storage barrel in the mounting barrel, the self-cleaning intercepting assembly is arranged in the vertical box on the side face of the guiding transverse hopper and can move downwards at regular time, and dirt intercepted by the self-cleaning intercepting assembly is removed through water resources collected by the storage barrel. The problems that in the prior art, most of field rainfall runoff automatic collecting devices are arranged on the downstream of a field rainfall path, in order to prevent field rainfall from being filtered, a filter screen structure is usually arranged in the automatic collecting device, and most of filter screen holes are blocked after the filter screen structure intercepts a certain amount of dirt, so that the field rainfall runoff automatic collecting device is damaged are solved. And the collection flow is obviously reduced.

The invention discloses a rainfall runoff simulation method based on a multi-station multivariable weather generator, which adopts a multivariable first-order autoregression model to generate rainfall and air temperature sequences, and can effectively simulate interannual change characteristics of the rainfall and air temperature sequences. The multi-variable multi-site weather generator can generate a hydro-meteorological element simulation field with correlation among sites, variables and time sequences, can effectively reflect all-around characteristics of a hydro-meteorological process, and improves a runoff simulation effect; by introducing an Empirical Copula post-processing method, under the condition that the simulation result of a weather generator is poor, the space-time correlation of a meteorological element simulation field is further improved, and more reliable input data is provided for a hydrological model.

The invention discloses a grid runoff forecasting method based on a space-time diagram convolutional network, and particularly relates to the technical field of runoff forecasting. The method comprises the following steps: firstly, performing grid division on a target drainage basin based on a landform and hydrological response unit, and constructing a space map structure; combining historical rainfall, runoff and remote sensing meteorological data to construct a space-time input feature sequence; extracting a spatial dependency relationship by using a graph convolutional network, extracting a time evolution feature by combining a long-short term memory network, and generating a node representation fused with the spatial-temporal feature; outputting a runoff prediction result of multiple time steps through a full-connection neural network; for a heavy rainfall event, input characteristics and a space structure can be updated in real time, dynamic completion and local graph structure reconstruction of missing data are realized, and finally a gridding runoff distribution graph in a future time period is generated. The method has high precision, high timeliness and high adaptability, and is suitable for runoff intelligent prediction in complex terrains and sudden heavy rainfall scenes.

The invention discloses a rainy day overflow pollution analysis and calculation method for a flood drainage pump station, and relates to the field of environmental science. According to the scheme, the method comprises the steps that the target pump station under each pump station type in multiple pump station types is determined based on the incidence relation between the forebay water quality of at least one target pump station and different rainfall types in the historical time period; on the basis of underlying surface interpretation of a catchment area of the target pump station under each pump station type in the different rainfall types, the pollution load of rainfall runoff of the target pump station under each pump station type in the different rainfall types is determined; based on the rainfall runoff pollution load of the target pump station under each pump station type in different rainfall types, the living source pollution load of the target pump station under each pump station type in different rainfall types, and the total pollution load of the target pump station under each pump station type in different rainfall types, the pollution load of the target pump station under each pump station type in different rainfall types is calculated; and determining the proportion of various overflow pollution sources of the target pump station under each pump station type in different rainfall types.

The invention discloses a flood forecasting method and system considering similarity and a multi-dimensional classification system, and the method comprises the steps: obtaining the historical rainfall and runoff observation data of a drainage basin, and building a historical sample library and a test period of a similarity flood forecasting model; analyzing the influence time lag of early rainfall and runoff on the forecast section runoff; constructing rainfall-runoff combined similarity indexes, establishing a multi-dimensional classification system, and dividing runoff change modes according to a three-dimensional classification system; a similarity flood forecasting model is constructed, the model is trained for different runoff change modes, and model optimal parameters and forecasting schemes of all the runoff change modes are obtained; and integrating various types of models, constructing a flood forecasting model for automatically identifying a runoff change mode, automatically matching rainfall-runoff similarity of optimal parameters of the model and a multi-dimensional classification system, and generating hourly runoff forecasting in the future 72 hours through rolling forecasting. According to the method, the flood forecasting precision and robustness under the complex hydrological condition are improved.

The invention relates to the technical field of data intelligent evaluation systems, in particular to a sponge city construction effect intelligent evaluation system and method based on data analysis, and the system comprises a data collection module which is used for collecting rainfall data, runoff data, water purification data and sponge facility operation and maintenance data of a sponge city construction area; the data processing module is used for processing and analyzing the data acquired by the data acquisition module; and the intelligent evaluation module is used for evaluating the sponge city construction effect based on the analysis result of the data processing module, and the data acquisition module comprehensively collects various data of the sponge city construction area and provides rich data sources for subsequent analysis. The data processing module abstracts a complex sponge city system into quantifiable indexes through a series of innovative mathematical models such as a rainfall-runoff topological matrix and sponge facility influence factors. And the intelligent evaluation module gives a final evaluation result based on the processed data.

The invention relates to the technical field of hydrology, and discloses a construction method of a watershed instantaneous unit line model based on a hydraulic energy complementation equation, a construction device of the watershed instantaneous unit line model based on the hydraulic energy complementation equation, a watershed instantaneous unit line determination method, a watershed instantaneous unit line determination device, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining sample rainfall runoff data of a sample drainage basin, constructing a basic model of a drainage basin instantaneous unit line model based on a hydraulic energy complementation equation, substituting the sample rainfall runoff data into the basic model, solving the basic model, performing parameter calibration by taking a precision index Nash efficiency coefficient as a target function, and obtaining a drainage basin instantaneous unit line model. And determining model parameters by maximizing the objective function, and substituting the model parameters into the basic model to obtain a watershed instantaneous unit line model based on the hydraulic energy complementation equation. The application range of the construction method of the instantaneous unit line is widened, and the accuracy of the construction result is improved.

The invention relates to the technical field of risk assessment, and discloses a small watershed disaster risk assessment system and method based on artificial intelligence, and the method comprises the following steps: synchronously obtaining real-time monitoring data such as rainfall, water level, soil moisture content and the like, associating equipment position information, verifying the water level change and rainfall-runoff relationship based on the fluid mechanics principle, and determining the risk of the small watershed disaster risk. Packaging and transmitting after abnormal values are filtered, integrating distributed node data, predicting the risk probability of disasters such as mountain torrents and landslides by using a lightweight model, aggregating edge node model parameters through federated learning, detecting and predicting reduction in precision in real time, triggering early warning, aggregating degradation signals, and quickly adjusting the model parameters by using physical constraint element learning. According to the invention, through an end-edge-cloud collaborative architecture and a physical constraint meta-learning mechanism, the real-time performance and accuracy of small watershed disaster risk assessment are improved, a modular architecture has lightweight deployment and cross-watershed expansion capabilities, and decision support of continuous evolution is provided for intelligent water conservancy projects.

The invention provides an intelligent watershed flood prediction method and system based on a multi-modal large model, and relates to the technical field of hydrology and water resources, and the method comprises the steps: S1, obtaining multi-modal original data from a meteorological radar, satellite remote sensing, a soil humidity sensor, hydrological station monitoring equipment and a terrain and land utilization database, and respectively performing signal denoising, spatial interpolation, dimension standardization and timestamp synchronization on the original data. According to the drainage basin flood intelligent prediction method and system based on the multi-modal large model, unified modeling of multi-modal information such as rainfall, runoff, soil water content and terrain is realized through deep fusion of multi-source heterogeneous data and large model structure design. The constructed time sequence processing subnet and the spatial feature extraction subnet are specially optimized for time sequence dependence and spatial topological relation respectively, the high dependence of a traditional physical model on data quality and boundary conditions is overcome, and the defects of a pure data driving model in the aspects of complex nonlinear relation and generalization ability are overcome.

The invention discloses a rainfall runoff simulation method fusing a knowledge graph and graph attention, and the method comprises the steps: taking an upstream and downstream relation as a directed graph, introducing a graph-based method, and learning the important function of the upstream and downstream relation in a rainfall runoff process; comprising the steps of using knowledge graph embedding to learn time-space correlation of upstream and downstream rainfall and using a graph attention network to learn upstream river channel confluence characteristics, improving a processing means of the model for large-scale multi-branch river hydrological data, and improving the learning ability of the model for a complex runoff sequence. The invention aims to improve the precision of the rainfall runoff simulation method, and particularly provides a new technical means for the fields of rainfall runoff simulation, flood forecasting, drainage basin water resource management and the like in the aspect of runoff prediction of large-scale multi-branch rivers.

The invention discloses a debris flow runoff threshold probability forecasting system construction method and a small watershed debris flow early warning method. The invention provides a debris flow runoff threshold probability forecasting system construction method for overcoming the defects of an existing dichotomy forecasting logic monitoring technology. According to the method, rainfall is regarded as a disaster clue, dimensionless single-width flow q * is regarded as a core variable, and a mapping relation among terrain parameters, q * and proportionality coefficients is established according to a model; the topographic parameters represent the influence of the topographic features on the disaster threshold in local and global meanings; the proportionality coefficient represents a q * induced soil position point starting proportion through a curve shape; and extracting a threshold corresponding probability from key parameter cumulative frequency analysis by using fitting generated data. According to the technical scheme, a complete rainfall runoff confluence input conduction model is established to be connected into a system construction core link, and a median particle size dynamic valuing method is adopted in the optimization scheme. The invention adopts a monitoring system threshold setting technical concept opposite to the prior art, is more scientific and rigorous, and has higher utilization value.

The invention relates to the technical field of rainfall runoff forecasting, in particular to a physical mechanism guided deep learning rainfall runoff forecasting method and related equipment, and the method specifically comprises the steps: obtaining meteorological data, underlying surface data and runoff data, and determining target features through statistical analysis and causal analysis; constructing a water balance long-short term memory neural network; performing model training based on the target features, a long-short term memory neural network of water balance and a Parzen estimator of a structure tree so as to determine hyper-parameters, model weight information and threshold information of a prediction model, and obtaining the prediction model; and forecasting rainfall runoff based on the prediction model. Therefore, on the basis of ensuring the precision performance of the model, the problem that the prediction does not conform to the physical consistency can be avoided, and the practicability of the model is improved.

This invention provides a multi-model fusion method, device, electronic device, and system for debris flow early warning. The method includes: collecting geographical and geological data, real-time rainfall, runoff, and auxiliary monitoring data; fusing a shallow flow numerical model, a moving least squares machine learning model, and a generalized regression neural network model, and obtaining a comprehensive runoff prediction through weighted least squares calculation; constructing critical runoff indicators based on the safety requirements of disaster-bearing bodies such as roadbeds, retaining walls, and culverts, and inversely calculating critical rainfall thresholds, then making differentiated adjustments based on the average gradient of the gully; generating a debris flow early warning report by comparing real-time predictions with thresholds, achieving final graded and zoned early warning for the warning area. This invention improves the accuracy and engineering relevance of early warnings by integrating physical mechanisms and data patterns, achieving refined early warning from "whether it will occur" to "when, where, and what the impact will be," while reducing the error of a single model and adapting to different terrains and disaster-bearing bodies along highways.