117 results about "Flood warning" patented technology

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to the technical field of flood early warning, and discloses a drainage basin flood early warning method based on regional data, which comprises the following steps: acquiring rainfall duration of a set region, and calculating a riverbed convergence rate of the set region as an auxiliary function to be fused into a preset BP neural network so as to construct a river reach runoff risk early warning model and output a river reach runoff risk level; the sediment inflow mode and the sediment content of a set area are obtained, the potential sediment risk of the river reach is determined, and the water and sediment risk of the river reach is further determined in combination with the rainfall and the sediment content; and taking actually monitored rainfall and forecast rainfall as driving data, obtaining a predicted water level through the distributed hydrological model, and issuing early warning in the set area in combination with the river reach water and sediment risk. According to the method, the river reach runoff risk early warning model is constructed, the sediment risk level identification mode is determined in combination with the sediment inflow mode, the sediment content and the actual rainfall, the water and sediment disaster risk assessment early warning method considering the sediment effect is provided, and the effectiveness and reliability of flood disaster prevention and control are improved.

The invention relates to an intelligent drainage basin management method, device and equipment based on digital twinning and a medium of the intelligent drainage basin management method and device based on digital twinning. The method comprises the steps that core terrain attributes are extracted through terrain feature decoupling and coding, and a disaster response function of an optimal source drainage basin is migrated based on terrain and rainfall similarity; a hydrodynamic model and a migration function are combined to generate a physically constrained synthetic disaster situation data set, a two-channel neural network model fusing topographic features and rainfall dynamics is constructed, and model parameters are dynamically calibrated through Bayesian continuous learning by using real-time monitoring data of a target drainage basin. Finally, a digital twinborn system with flood routing prediction capability is formed, the problem of rapid construction of a flood prediction model under the condition of no historical data is solved, and the timeliness and accuracy of early warning of sudden flood in small and medium-sized watersheds are remarkably improved.

The invention discloses a flood disaster early warning decision optimization method and device, a medium and a product, and relates to the field of environmental disaster management and prediction.The method comprises the steps that a decision feature matrix is determined according to a multi-dimensional decision situation; determining certainty and other values according to the decision feature matrix, and dynamically calibrating psychological parameters; a Gini index is improved according to the decision feature matrix and the weight of the flood event occurrence probability, and a split decision tree model is constructed based on the improved Gini index; determining a key split point on the flood event occurrence probability according to the trained split decision tree model; determining an adaptive decision threshold according to the flood event occurrence probability and the key split point; determining a framework effect compensation factor; adjusting an adaptive decision threshold by using the framework effect compensation factor; and determining a flood disaster early warning decision according to the adjusted threshold value. According to the invention, the accuracy of flood early warning decision and the system robustness can be improved.

The present invention proposes a flood monitoring and early warning method and system based on the fusion of multi-source remote sensing satellite data. The present invention collects various types of remote sensing satellite data of flood areas; pre-processes the collected remote sensing satellite data; integrates the pre-processed multiple remote sensing satellite data with features of different scales to obtain feature fusion; generalizes the feature fusion based on the revisited channel attention module from the same-layer scene to the cross-layer scene; utilizes the fused remote sensing images to identify floods and extract the boundaries and scope of the flood area; conducts flood warnings based on the identified flood areas and historical flood data, combined with meteorological and hydrological information, to obtain warning results; and conducts a comprehensive analysis of the causes, processes, and impacts of flood events. By integrating multi-source satellite data, the present invention aims to improve the accuracy of large-scale flood detection and enhance the ability to predict flood trends, thereby ensuring the accuracy and reliability of spatiotemporal flood warnings.

The invention relates to the field of hydrological forecasting, and discloses a distributed hydrological model construction method, system and equipment based on feature units, and the method comprises the steps: building a session flood historical data set according to the collected topographic data, soil data, hydrological data and meteorological data, dividing the topographic data in the session flood historical data set into feature units, the other soil data, hydrological data and meteorological data are matched to each feature unit, and a spatial topological relation is constructed based on the feature units; constructing a distributed hydrological model based on the spatial topological relation to calibrate runoff production model parameters, and calibrating confluence model parameters according to a runoff production model parameter calibration result; and according to a parameter calibration result, various monitoring and forecasting information and different levels of flood early warning release standards, manufacturing and releasing flood early warning information. According to the method, the distributed hydrological model is constructed based on the feature units, and real-time flood operation forecasting can be carried out.

The invention relates to the technical field of hydrology and water conservancy, and particularly discloses an intelligent real-time watershed flood early warning method and system, and the method comprises the steps: watershed data acquisition, fusion forecasting model construction, multi-stage early warning threshold calculation, early warning generation and online evaluation. According to the scheme, the real-time residual error of the physical hydrological model is constructed, the complex mode that the residual error changes along with the real-time rainfall intensity and the drainage basin state is learned by adopting the residual error prediction model based on machine learning, and an interpretable and steady forecast baseline and high-precision error correction are provided through a forward coupling and feedback coupling mixed architecture; the overall robustness of flood forecasting is effectively improved; dynamic inversion is carried out by using a fusion forecasting model, flood risk differences under different soil conditions are identified, the matching degree of early warning signals and actual risks is greatly improved, a comprehensive risk value is calculated in real time on a unified grid division system, and a risk zoning map is refreshed in real time along with updating of rainfall forecasting, so that the real-time prediction of the flood risk is realized. And the practicability of early warning information and the pertinence of emergency response are effectively improved.

A method for evaluating terrain uncertainty in flood warning and forecasting is provided. The method includes: S1, acquiring three types of digital elevation model (DEM) data from a shuttle radar topography mission (SRTM), an advanced spaceborne thermal emission and reflection radiometer (ASTER), and an advanced land observing satellite (ALOS); and preprocessing the three types of DEM data; S2, optimizing urban terrain characteristics; S3, constructing a multidimensional parameter space by using Latin hypercube sampling (LHS); S4, calculating flood hydrodynamics numerical value based on multidimensional sample points; and S5, constructing a global sensitivity analysis method frame suitable for urban terrain characteristics-related factors, where a Sobol quantitative method is used in the global sensitivity analysis method frame, and the Sobol quantitative method is used to evaluate uncertainties and sensitivity characteristics of multiple factors of terrain data based on a variance decomposition theory.

The invention discloses a flood priority scheduling intelligent evaluation method for multi-modal flood early warning. The method comprises the following steps: step 1, rainfall, water level and flow velocity data are collected and preprocessed to generate standardized multi-modal input; 2, constructing a river network graph structure, and extracting node and edge features to generate graph structure input data; step 3, inputting the graph structure into an EvolveGCN evolutionary graph convolutional network, embedding scheduling path accessibility, resource bottleneck and execution constraint features, and outputting a state propagation feature set; step 4, generating a node state prediction vector according to the state propagation characteristics; 5, calculating three types of risk scores, and applying nonlinear constraints to construct a priority score set; step 6, generating a scheduling action sequence and collecting feedback data; and step 7, updating the convolution structure by using the feedback updating score and the scheduling sequence, and outputting a final instruction. The flood scheduling intelligent prediction closed loop is realized, and the response efficiency and the scheduling feasibility are improved.

The invention relates to a flood early warning and valve linkage control system based on the Internet of Things. A river topology construction module dynamically generates a topology network representing a river structure by fusing geographic information and real-time sensing data; the early warning weight calculation module generates a quantitative risk index in combination with the water level overrun degree and the historical flood probability; the water flow time matrix generation module calculates a flood propagation time sequence based on a hydraulic evolution model; the flood early warning control module synchronously outputs an early warning report and a valve control instruction through a space-time risk coupling mechanism to realize intelligent linkage of early warning and control; the topology model correction module dynamically optimizes the river channel parameters and the risk model according to the valve execution feedback. According to the system, the problems of early warning response lag, model prediction misalignment and extensive valve control in traditional flood control are solved, early warning and control collaborative decision is driven through data closed loop of the Internet of Things, and the accuracy and reliability of flood control response are improved.

A flood warning apparatus may include: an extendable body that extends above a foundation, where the extendable body is configured to move within a range bounded by a minimum height and a maximum height based on a level of water in a waterway that covers the foundation; a platform disposed atop the extendable body, wherein the platform is buoyant; and. a floatation object having a notification mechanism, wherein the floatation object is configured to be retained by a retention feature of the platform when the position of the extendable body is less than the maximum height, where the floatation object is further configured to be released from the platform by the retention feature when the level of water in the waterway exceeds a threshold value, where the floatation object is buoyant, and where the notification mechanism activates when the floatation object is released from the platform by the retention feature.

This invention relates to an airport rainfall and flood early warning method, electronic device, and medium based on a large language model, for use in sponge airports. The early warning method includes: based on a rainstorm and flood management model, using a large language model for data augmentation to generate a multi-factor flood early warning dataset containing regionally specific factor data and airport-specific factor data; based on a retrieval-enhanced generation mechanism, vectorizing and calculating feature bias on the current input data, retrieving the most relevant historical case context information from the multi-factor flood early warning dataset; adaptively fusing the multi-factor features and the most relevant historical case context information, extracting key information through multi-head self-attention and attention pooling, and outputting the flood risk level and early warning result. This invention can significantly improve the accuracy, real-time performance, and interpretability of flood risk assessment, providing more reliable, traceable, and intelligent flood early warning and decision support for sponge airports.

The application relates to the technical field of agricultural early warning, in particular to a multi-stage linkage agricultural flood early warning information automatic broadcasting system, and the following scheme is provided: a sensing terminal collects farmland images, combines environmental data, analyzes waterlogging features by using a graph neural network, and thus precise flood disaster risk assessment is realized. After disaster identification, the system dynamically adjusts the broadcasting range and the communication protocol according to the risk level, so as to ensure that the early warning information is rapidly transmitted to the receiving terminal. In addition, an iterative operation mechanism is adopted, a cluster head terminal is selected according to the consistency of the waterlogging features, information redundancy transmission is avoided, and network resource use is optimized. The application improves the real-time performance, accuracy and resource utilization efficiency of the flood disaster early warning system, and has obvious innovation and application value.

This invention relates to a method and system for real-time flood warning in watersheds based on multi-source data fusion, belonging to the field of flood warning technology. The method includes: acquiring multiple multi-source data from a target watershed and preprocessing the data; fusing the preprocessed multi-source data to obtain fused data; and inputting the fused data into a trained real-time flood warning model for processing to obtain flood warning results for the target watershed. The real-time flood warning model is trained based on a CNN-LSTM model. During the training process, a Bayesian optimizer is used to optimize the hyperparameters of the CNN-LSTM model so that the feature flattening layer in the real-time flood warning model flattens the input multi-dimensional features into a one-dimensional vector based on the optimized hyperparameters, thereby improving the accuracy and timeliness of flood warnings.

The present application relates to the technical field of hydrological resources, and specifically includes an intelligent short-impending fusion flood forecasting method based on a rain measurement radar, which comprises: receiving rain measurement detection information, performing prediction effect feedback in combination with a short-impending flood monitoring task at an edge adaptive correction node, configuring flood identification features based on a hydrological fusion prediction model, determining a flood peak time combination and a flooding risk, and generating a graded short-impending flood warning signal. The technical problem of the initial configuration of the hydrological fusion prediction model not matching the rain measurement data characteristics and the prediction target, the prediction deviation being unable to be corrected in time, and the deviation accumulation affecting the prediction reliability is solved by using a general hydrological model deployment mode, the technical effect of adaptively deploying the hydrological fusion prediction model by binding the core detection information of the rain measurement radar and the flood prediction accuracy requirement, the cooperative mechanism of the edge adaptive correction node and the RS485 bus interface, effectively avoiding deviation accumulation, and improving the stability of runoff simulation is achieved.

The invention provides a method and device for issuing flood early warning in advance through upstream and downstream linkage, belongs to the field of flood early warning, and is used for solving the problem that flood early warning is difficult to accurately, timely and reliably carry out in related technologies. The water level of upstream and downstream monitoring points of a river basin and key features of flood influence factors are intelligently analyzed by utilizing a self-research algorithm model, flood early warning scores are analyzed step by step and emphatically by utilizing the key features, the result of the flood early warning scores obtained by final analysis is ensured to be more accurate and reliable, flood early warning is issued in advance by utilizing the flood early warning scores, and the flood early warning efficiency is improved. The flood early warning can be timely, accurately and reliably issued in advance, so that the purpose of reducing and even avoiding economic loss and casualties caused by flood is achieved.

The invention provides a flood prediction method, device and equipment for a mountainous area hydropower station. The method comprises the steps of obtaining meteorological data in a drainage basin where the mountainous area hydropower station is located in real time; the meteorological data comprises rainfall data, flow data and water level data; respectively inputting the meteorological data into a plurality of pre-constructed flood prediction models, and determining a prediction result of each flood prediction model in the current period; wherein the flood prediction model comprises a time sequence model, a hydrological model and an LSTM and runoff coefficient model; determining a prediction error of each flood prediction model according to the prediction result and the water level data in the current period; according to the prediction error, determining a flood prediction model adopted in the next period and a corresponding prediction result; and issuing corresponding flood early warning according to the prediction result of the next period. According to the invention, through a multi-model parallel prediction and dynamic selection mechanism, the adaptation shortages of a single model under a complex water regimen are effectively made up, and the prediction error is further reduced.

The invention discloses a flooding alarm system and a flooding alarm method. The flooding alarm system comprises a sensing device and an alarm device. The sensing device is provided with a waterproof shell and a containing space and used for detecting the height of accumulated water flowing into the containing space through a plurality of water inlet holes in the waterproof shell. The sensing device stores a plurality of instructions respectively corresponding to a plurality of flooding heights, and outputs an instruction corresponding to the flooding height in response to detecting that the height of the accumulated water flowing into the accommodating space accords with one of the flooding heights. And the alarm device is in wireless communication with the sensing device, responds to the received instruction output by the sensing device, and executes an alarm program corresponding to the instruction. Therefore, the alarm device and the sensing device of the flooding alarm system provided by the invention are separated, and the alarm device can execute alarm programs of different levels according to different heights of accumulated water detected by the sensing device.

The invention discloses a small reservoir flood early warning method based on a water conservancy rainfall radar, and relates to the technical field of flood early warning, and the method comprises the following steps: building a radar rainfall calculation model; prediction radar data are obtained through prediction by adopting a short-term rainfall prediction model based on a deep learning model and are input into the radar rainfall calculation model, so that the near extrapolation predicted rainfall is obtained; combining the short-term numerical forecast rainfall with the adjacent extrapolation forecast rainfall to obtain short-term and imminent forecast rainfall; adopting a fusion model based on a long-short-term neural network and a three-water-source Xinanjiang model to obtain a predicted reservoir flood volume; and according to the predicted reservoir flood volume, calculating the positively calculated rain accepting capability of the small reservoir in a rolling manner and carrying out flood early warning. According to the method, the rainfall prediction accuracy and the reservoir flood volume prediction accuracy are improved by combining the short-time numerical forecast rainfall with the near extrapolation forecast rainfall and fusing the long-short-term neural network and the three-water-source Xinanjiang model, so that the reliability of flood early warning is improved.

The invention discloses an AI-based urban river water level dynamic prediction and flood early warning system and method, and belongs to the technical field of urban intelligent flood prevention. The system comprises a system dynamic characteristic analysis module used for constructing urban state digital twin and calculating a system response fingerprint vector representing system dynamic response characteristics; the risk decoupling and quantification module is used for comparing the system response fingerprint vector with a healthy elastic fingerprint template to determine a fingerprint distortion vector and decoupling and outputting a multi-dimensional risk vector; the self-adaptive decision-making module is used for determining a target action scheme based on the multi-dimensional risk vector and the decision-making cost; and the workflow arrangement engine is used for executing the workflow corresponding to the target action scheme. According to the invention, through real-time analysis of system response fingerprint distortion, more accurate and foresight flood early warning and decision making are realized.

A report generator is provided that produces informational reports during an identified event or incident. For example, for a given a declared event, such as a flood warning, relevant data is gathered, such as the event declaration, current and forecast weather, power outages and predictions, news and social media. This data is sent to the report generator for analysis, which is a large language model. A defined or selected area may be used so that the data is relevant to the selected area, which may also be subject to the identified event. Users are able to view the generated reports on a platform or have the reports sent via alerts as soon as they are created. The generation of summaries of discussions in a collaboration forum are also provided. Incident resource analysis techniques provide determinations of, for example, resource-at-risk needs for an event.

The application provides a water level prediction method based on an integrated intelligent model, and relates to the technical field of water regime prediction. The method comprises the following steps: obtaining historical hydrological data and pre-processing by using improved Z-score standardization; constructing a long short-term memory network-sequence to sequence model, a support vector regression model and a Transformer model based on a multi-head attention mechanism as basic learners, and performing data processing and prediction on the learners according to a set input-output process; combining the prediction results of the three learners into a feature vector, training a meta-learner by using a Stacking method, and forming an integrated model for final water level prediction. The application can comprehensively capture long-term and short-term dependencies and complex nonlinear characteristics in a flood flow time series, improve prediction accuracy, robustness and calculation efficiency, and enhance the interpretability of the prediction results, thereby providing reliable technical support for flood warning.

The invention discloses a flood early-warning decision analysis method based on deep learning, and relates to the technical field of flood early-warning decision, and the method comprises the steps: collecting and aligning rainfall, water level, flow, engineering working condition and exposure data, generating probabilistic risk degree and impact evaluation through combining with a flood or waterlogging prediction module, and carrying out the early-warning decision analysis. Constructing risk-influence objects indexed according to space units and time slices; response telemetering is formed based on early warning sending, reading and handling receipts, and a credibility state and an attention budget state are recursively derived according to crowds and channels; under security constraints and attention budget constraints, candidate early warning strategies including early warning levels, publishing objects, publishing channels and action suggestion packets are graded and screened, graded pushing and disposal distribution are implemented according to selected early warning publishing strategies, relevant parameters are updated through auditing records and post-disaster true value recharge, and the early warning publishing strategies are distributed according to the updated parameters. Therefore, the conversion efficiency of the early warning action is improved while the invalid alarm and the disposal consumption are reduced.

The application belongs to the technical field of flood warning, and in particular to a flood control large model warning method and system based on multi-source data fusion and dynamic optimization, which comprises a data input layer, a core model processing layer and a decision output layer; the data input layer is composed of a data acquisition module and is used for building a multi-dimensional space-time data system and acquiring multi-source heterogeneous data; the core model processing layer is composed of a data preprocessing and fusion module, a prediction model module and a dynamic optimization module and is used for processing, fusing, predicting and online optimizing the multi-source heterogeneous data; the application can quickly adapt to unique geographical and climatic characteristics by using only a small amount of local sample data, can automatically generate and accurately release differentiated action guidelines for different regions and different objects, can directly convert warning information into executable emergency instructions, and can significantly improve the intelligent level and decision efficiency of emergency response.

The landslide damming disaster chain cooperative early warning method and system provided by the application relates to the field of disaster early warning. The method comprises the following steps: receiving landslide shock signals and water level signals in real time; obtaining time and position information of landslide occurrence according to landslide shock signal analysis, and issuing a large landslide warning; evaluating the possibility of river blocking caused by landslide according to the spatial relationship between landslide and river system; if there is a possibility of river blocking caused by landslide, issuing a secondary warning of river blocking, and combining the water level signal to determine whether the river has been blocked; if the river has been blocked, issuing a primary warning of river blocking, and positioning the blocking point according to the spatial relationship between landslide and river system; evaluating the risk of landslide damming backwater and flood outburst according to the water level signal, and issuing a corresponding grade of flood warning. The application actively issues landslide damming stage identification and early warning information by timely and accurate identification of the disaster link and severity of landslide damming, which helps to automatically identify the disaster process of the landslide damming disaster chain.

The embodiment of the invention provides a flood early warning method and device based on insurance policies, electronic equipment and a medium, belongs to the technical field of data analysis, and is suitable for financial scenes. The method comprises the steps of obtaining a target underwriting drainage basin of a target insurance policy, dividing the target underwriting drainage basin into grid drainage basin units, performing unit rainfall extremum analysis according to regional historical rainfall, performing unit rainfall simulation according to historical claim settlement loss levels and unit rainfall extremum data, and performing unit rainfall simulation according to the historical claim settlement loss levels and unit rainfall extremum data. And unit flood range prediction is performed according to the unit forecast rainfall data and the flood inundation range simulation data, and flood early warning is performed based on the flood inundation range prediction data. According to the embodiment of the invention, the unit rainfall simulation is carried out according to the historical claim settlement loss level and the unit rainfall extreme value data, and the flood early warning is carried out according to the unit forecast rainfall data and the flood inundation range simulation data, so that the early warning fatigue problem caused by sending invalid flood early warning to low-risk customers is effectively avoided, and the flood early warning precision is improved.

This application discloses a method, device, medium, and product for optimizing flood disaster early warning decision-making, relating to the field of environmental disaster management and prediction. The method includes: determining a decision feature matrix based on a multi-dimensional decision-making context; determining deterministic values ​​based on the decision feature matrix and dynamically calibrating psychological parameters; improving the Gini index based on the weights of the decision feature matrix and the probability of flood events, and constructing a split decision tree model based on the improved Gini index; determining key split points on the probability of flood events based on the trained split decision tree model; determining an adaptive decision threshold based on the probability of flood events and the key split points; determining a framing effect compensation factor; adjusting the adaptive decision threshold using the framing effect compensation factor; and determining the flood disaster early warning decision based on the adjusted threshold. This application can improve the accuracy and robustness of flood early warning decision-making.

The application relates to the field of urban water environment and pollution control technology, and discloses a new pollutant simulation method for urban river network, which comprises the following steps: obtaining hydrographic data of a target river network region, collecting concentration measured data of a target pollutant in water and sediments, constructing a coupled hydrology-pollutant transmission model, setting initial parameters of the model, and outputting the spatial and temporal distribution of the pollutant by running the model; emergency simulation of a sudden pollution event, wherein the influence range is output within 10 minutes after inputting accident discharge parameters; long-term pollution trend prediction, which supports scenario analysis on a 5-10 year scale. A city drainage correction term is introduced by improving the Saint-Venant equation set, and the parameters are optimized in combination with measured data, so that the accuracy of hydrological simulation of the urban river network is significantly improved; compared with a traditional method, the Nash efficiency coefficient is improved from 0.68 to 0.79, so that the simulation of the water dynamics of the urban river network is more accurate, and a more reliable basis is provided for the design of the urban drainage system and flood warning.

The invention provides an analysis, early warning and release system based on a flood inundation condition, and relates to the technical field of flood early warning, the system comprises a constant flow special monitoring module, a probability forecast analysis module and a flood discharge risk analysis module, and the constant flow special monitoring module collects river data and rainfall data; judging whether flood analysis is started or not through risk index calculation; the probability forecast analysis module determines a weight factor based on historical data of each prediction model, obtains water level prediction data in combination with real-time water surface change data, and calculates a water area risk index to realize flood early warning; the flood discharge risk analysis module evaluates a flood discharge point use index according to historical flood discharge data, sets a monitoring point, calculates a flood discharge demand index by combining a water level, embankment stress and the like, and performs early warning on a risk flood discharge point, the system has multi-module cooperation and multi-dimensional analysis, flood risk early warning and flood discharge decision can be accurately realized, and the flood disaster prevention and control capability is effectively improved.

A flood rapid evolution and inundation simulation method based on a 1D-CNN algorithm, first, a two-dimensional hydrodynamic model of the research area is constructed, and the model roughness parameters are calibrated and verified; second, the two-dimensional hydrodynamic model is run to simulate the flood evolution and inundation simulation process under different historical working conditions, and the inundation water depth simulation results output by the model are extracted; then, the input and output structure of the 1D-CNN model is determined, and the training, verification and test sample sets are generated, the flood rapid evolution and inundation simulation model based on the 1D-CNN algorithm is established, and the internal weight parameters of the model are trained and optimized; finally, the application effect of the 1D-CNN model is tested, the simulation results of the two-dimensional hydrodynamic model are taken as the benchmark, the flood inundation simulation and prediction effect of the 1D-CNN model established and trained are compared and analyzed, and the accuracy and timeliness of the 1D-CNN model are evaluated. The present application can effectively solve the problems of long calculation time, large consumption and poor stability of the traditional two-dimensional hydrodynamic model, quickly and accurately simulate the flood evolution and inundation situation, meet the application requirements of real-time flood forecasting, and provide technical support for flood early warning and simulation work.