Extreme climate flood evolution simulation and risk warning platform based on deep learning

Abstract

This invention, titled "Extreme Climate Flood Evolution Simulation and Risk Early Warning Platform Based on Deep Learning," belongs to the field of hydrometeorological technology. The technical problem it addresses is the inability of traditional hydrological models to adaptively respond to the nonlinear hydrological effects caused by extreme precipitation. The key technical solution includes a data acquisition module, a flood evolution simulation module, a real-time monitoring module, an early warning issuance module, an adaptive optimization module, and a report generation module. In simulating the evolution of extreme climate floods, the deep learning model unit in the flood evolution simulation module employs a fusion architecture of convolutional neural networks and long short-term memory networks to adaptively learn the nonlinear hydrological effects under extreme precipitation events. Combined with the Bayesian optimization method in the adaptive optimization module, the model parameters are dynamically adjusted, overcoming the bias problem of fixed-parameter models in events exceeding historical records.

Description

Technical Field

[0001] This invention belongs to the field of hydrological and meteorological technology, specifically involving a deep learning-based platform for simulating the evolution of extreme climate floods and providing risk warnings. Background Technology

[0002] Currently, the industry's real-time simulation and risk warning of flood evolution under extreme weather conditions mainly rely on traditional hydrological models. These models are based on fixed physical parameters. However, extreme weather events are characterized by their suddenness and uncertainty. Traditional hydrological models with fixed parameters cannot adaptively respond to the nonlinear hydrological effects brought about by extreme precipitation, resulting in significant technical limitations in practical applications. When encountering extreme precipitation events such as record-breaking torrential rains, the flood evolution simulation results output by traditional hydrological models deviate significantly from the actual flood situation, failing to guarantee the accuracy of flood risk warning signals. Furthermore, the inaccuracy of warning signals leads to a lack of effective technical guidance for subsequent flood disaster emergency response, hindering precise prevention and control of extreme weather flood disasters and failing to effectively reduce the loss of life and property caused by floods.

[0003] Relevant patent documents retrieved:

[0004] This document, published in China (CN120278461A) on April 2, 2025, discloses a deep learning-based digital twin system for generating flood disaster early warning and emergency response plans for watersheds. The system includes a data acquisition and processing module, a digital twin modeling module, a deep learning prediction module, an emergency response generation module, and a system feedback module. The data acquisition and processing module acquires and standardizes multi-source heterogeneous data (meteorology, hydrology, topography, remote sensing) of the watershed in real time, providing real-time multi-source heterogeneous data input. The digital twin modeling module constructs a digital twin model based on the real-time multi-source heterogeneous data to simulate the dynamic hydrological processes of the watershed and outputs the simulation results. The deep learning prediction module receives the simulation results from the digital twin model, trains the prediction model, and outputs predictions of the spatial and temporal distribution of flood disasters. The emergency response generation module receives the prediction results output by the deep learning prediction module and generates flood emergency response plans based on these results. The system feedback module evaluates the effectiveness of the prediction and response schemes based on the flood emergency response plans and prediction results, and provides a basis for adjusting future disaster early warnings.

[0005] The prior art represented by the aforementioned documents has at least the following unresolved technical problems or defects: Existing technologies only use a digital twin modeling module combined with a deep learning prediction module to predict flood disasters and generate emergency plans. They do not disclose a dedicated simulation module for flood evolution under extreme climate conditions or a corresponding refined prediction technology solution. They cannot achieve adaptive learning of nonlinear hydrological effects under extreme precipitation events, nor do they design a dynamic optimization mechanism for model parameters for extreme climate events that exceed historical records. The relevant evidence is that the core of the existing technology is a hierarchical prediction architecture that combines a digital twin model with a deep learning prediction model. It only completes the basic prediction of the spatiotemporal distribution of flood disasters and generates emergency plans. It does not involve the accurate simulation of the flood evolution process, the dynamic adjustment of model parameters under extreme climate conditions, or the design of hydrodynamic calculation related technologies adapted to extreme climates. It is difficult to adapt to the suddenness and uncertainty of extreme climate floods, and the simulation results are prone to deviation from the actual situation.

[0006] Meanwhile, existing technologies lack a closed-loop architecture covering the entire process from data acquisition, simulation and prediction, real-time monitoring to early warning issuance and model optimization. They also lack dedicated technical designs for real-time monitoring of flood evolution, rapid anomaly identification, and multi-channel early warning issuance. Furthermore, they lack a corresponding adaptive optimization module to continuously fine-tune the model, making it impossible to guarantee the accuracy and timeliness of flood warnings under extreme weather conditions. The relevant evidence is that existing technologies only set up a system feedback module for post-event evaluation of contingency plans and prediction effects, without designing a real-time monitoring sensor network, data fusion, and anomaly detection unit. They also lack a dynamic optimization mechanism for model parameters based on methods such as Bayesian optimization, making it difficult to achieve real-time monitoring of flood evolution under extreme weather conditions and dynamic adjustment of early warning parameters.

[0007] In view of this, the present invention is hereby proposed. Summary of the Invention

[0008] To address the aforementioned technical problems in the existing technology, this invention provides a deep learning-based platform for simulating and warning the evolution of extreme climate floods, thus resolving the issue raised in the background technology of the inability to adaptively respond to the nonlinear hydrological effects caused by extreme precipitation.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows: A deep learning-based platform for simulating the evolution of extreme climate floods and providing risk early warning includes: The data acquisition module is used to acquire multi-source meteorological data, geographical parameters, and basic data related to historical floods; The flood evolution simulation module receives the basic data output by the data acquisition module and is used to perform flood simulation operations, generate flood evolution data, and output early warning data. The real-time monitoring module receives the early warning data output by the flood evolution simulation module, and is used to collect real-time environmental data, integrate multi-source monitoring information, and output monitoring results. The early warning release module receives the monitoring results output by the real-time monitoring module and is used to generate early warning instructions, release early warning information, and adjust early warning parameters. The adaptive optimization module receives the early warning data output by the flood evolution simulation module, and uses it to evaluate the model simulation accuracy, fine-tune the model parameters, and generate model optimization instructions. The report generation module integrates the early warning data and monitoring results to generate flood risk visualization results and output a flood risk assessment report.

[0010] Furthermore, the data acquisition module includes: Meteorological data acquisition unit: used to acquire multi-source meteorological data; Terrain data integration unit: used to set geographic parameters; Historical flood database unit: used to store flood evolution path data under historical extreme climate events, and compare it with current meteorological data through a spatiotemporal matching unit; the spatiotemporal matching unit is used to match the currently monitored meteorological data with the event data in the historical flood database; The satellite remote sensing data parsing unit is used to extract precipitation distribution information through multispectral imaging technology and generate remote sensing precipitation data; The ground monitoring station data integration unit is used to receive the remote sensing precipitation data, combine it with the measured data collected by the rain gauge and the flow velocity sensor, and output calibrated multi-source meteorological data through the data calibration algorithm.

[0011] Furthermore, the flood evolution simulation module includes: Deep learning model unit: It adopts an architecture that integrates convolutional neural networks and long short-term memory networks. The convolutional neural network is used to extract spatial features of terrain elevation and river morphology, and the long short-term memory network is used to model the time series dependence of precipitation trends and flood propagation delay. Hydrodynamics calculation unit: Based on the Saint-Venant equations, it solves the flood evolution process and optimizes the model parameters through the gradient descent algorithm to generate flood evolution data; Risk assessment unit: Used to calculate the depth and extent of flood inundation using probabilistic statistical methods, and outputs a risk level as early warning data in combination with population density data.

[0012] Furthermore, the real-time monitoring module includes: Sensor Network Unit: Employs a distributed IoT sensor array deployed at key nodes in the river channel to collect real-time environmental data through water level sensors, flow velocity sensors, and rainfall sensors; Data fusion unit: It uses the Kalman filter algorithm to integrate real-time environmental data collected by multiple sensors and generate fused environmental information; Anomaly detection unit: Based on the isolated forest algorithm, it identifies data anomalies in the fused environmental information and outputs anomaly monitoring results.

[0013] Furthermore, the early warning release module includes: Warning level classification unit: used to classify warning instructions into three levels: red, orange, and yellow, according to the flood risk level classification standard; Communication unit: Used to issue early warning information through dual channels of 5G network and satellite communication; Feedback mechanism unit: Used to receive user feedback data, analyze warning-related data through machine learning algorithms, and dynamically adjust warning parameters.

[0014] Furthermore, the adaptive optimization module includes: Model optimization unit: Using Bayesian optimization methods, the prediction accuracy of the deep learning model is evaluated periodically; Parameter tuning unit: Through sensitivity analysis, key parameters affecting simulation accuracy are identified, and parameters of the hydrodynamic calculation unit are optimized. Algorithm update unit: used to trigger the model retraining process when model drift is detected, and to generate model optimization instructions using the new data.

[0015] Furthermore, the report generation module includes: Visualization Unit: Generates interactive risk maps based on WebGL technology, supports 3D flood inundation simulation, and produces flood risk visualization results; Report generation unit: Used to integrate historical flood data with real-time monitoring results, and automatically generate a structured flood risk assessment report through natural language processing technology. The assessment report includes flood evolution trend, risk distribution map and emergency recommendations.

[0016] Furthermore, it also includes a data preprocessing module, which is connected between the data acquisition module and the flood evolution simulation module, and specifically includes: Data cleaning unit: Uses box plot method to identify outliers in the raw data and remove them; Standardization Unit: The Z-score normalization algorithm is used to perform scale unification processing on the cleaned data, and the standardized data is output to the flood evolution simulation module.

[0017] Furthermore, it also includes a user interaction module, which includes: The graphical user interface unit is used to receive user-input query commands, display flood risk visualization results and early warning information, and supports user-defined flood simulation parameters. It can also adjust the flood evolution model input based on the user-defined parameters and update the simulation results in real time.

[0018] Furthermore, the platform integrates blockchain technology and is configured with a distributed ledger unit. The distributed ledger unit is used for secure data storage and recording of all data operation logs on the platform. The distributed ledger unit is linked with the report generation module to provide audit trail functionality for flood risk assessment reports.

[0019] The beneficial effects of this invention are as follows: (1) When simulating the evolution of extreme climate floods, the deep learning model unit in the flood evolution simulation module adopts a fusion architecture of convolutional neural network and long short-term memory network to adaptively learn the nonlinear hydrological effects under extreme precipitation events. Combined with the Bayesian optimization method in the adaptive optimization module, the model parameters are dynamically adjusted, which can overcome the bias problem of traditional fixed parameter models in events exceeding historical records, ensure the consistency between flood simulation results and actual conditions, and improve the accuracy and reliability of evolution prediction under extreme climate conditions.

[0020] (2) When conducting real-time flood risk warning, multi-source environmental data is integrated through the sensor network unit and data fusion unit in the real-time monitoring module, and flood signs are quickly identified by the anomaly detection unit. At the same time, the feedback mechanism unit in the warning release module corrects the warning parameters in real time based on machine learning algorithms, which can reduce the warning lag problem caused by simulation deviation, ensure the closed-loop response efficiency from data collection to information release, and enable flood warning signals to guide emergency actions in a timely manner and reduce disaster losses.

[0021] (3) When conducting flood risk assessment and decision support, an interactive risk map is generated through the visualization unit in the report generation module, and the user interaction module supports the adjustment of custom parameters to achieve accurate risk assessment under dynamic changes of the disaster-bearing body. At the same time, the structured report is automatically output using natural language processing technology, which can solve the problem of static assessment in the existing system and improve the dynamic adaptability of flood risk management and the practicality of early warning effect. Attached Figure Description

[0022] Figure 1 This is an architecture diagram of the extreme climate flood evolution simulation and risk early warning platform provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0024] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0025] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0026] Example 1 See Figure 1 , Figure 1 This is an architecture diagram of the deep learning-based extreme climate flood evolution simulation and risk early warning platform proposed in this invention, which may specifically include: M1, the data acquisition module, is used to acquire multi-source meteorological data, geographical parameters, and basic data related to historical floods; specifically, it includes: M11, Meteorological Data Acquisition Unit: Used to acquire multi-source meteorological data; M12, Topographic Data Integration Unit: Used to set geographic parameters; geographic parameters include but are not limited to: digital elevation model data, river cross-section geometry data, surface roughness coefficient, soil type and permeability, land use and land cover data.

[0027] M13, Historical Flood Database Unit: Used to store flood evolution path data under historical extreme climate events, and compare it with current meteorological data through the spatiotemporal matching unit; The spatiotemporal matching unit is used to match currently monitored meteorological data with event data in the historical flood database; the comparison workflow is as follows: First, the feature vector of the current meteorological event is extracted; second, the most similar historical flood events are retrieved from the historical database using a dynamic time warping algorithm; finally, the flood evolution path and inundation range of similar historical events are output as prior knowledge and reference benchmarks for the current simulation.

[0028] M14, Satellite Remote Sensing Data Analysis Unit, is used to extract precipitation distribution information and generate remote sensing precipitation data through multispectral imaging technology; M15, the ground monitoring station data integration unit, is used to receive the remote sensing precipitation data, combine it with the measured data collected by the rain gauge and the flow velocity sensor, and output calibrated multi-source meteorological data through the data calibration algorithm.

[0029] The core of the calibration algorithm is a deviation correction method based on linear regression, which uses ground-measured data as a benchmark to perform statistical fitting on the remote sensing data. The specific formula is as follows:

[0030] in, This represents the original remote sensing precipitation value. For the calibrated data, and For coefficients; Subsequently, the unit integrates the calibrated remote sensing data with real-time ground monitoring data to generate spatially continuous and temporally consistent multi-source meteorological data.

[0031] M2, a flood evolution simulation module, receives basic data output from the data acquisition module and is used to perform flood simulation operations, generate flood evolution data, and output early warning data; specifically including: M21, Deep Learning Model Unit: It adopts an architecture that integrates convolutional neural networks and long short-term memory networks. The convolutional neural network is used to extract spatial features of terrain elevation and river morphology, and the long short-term memory network is used to model the time series dependence of precipitation trends and flood propagation delay. M22, Hydrodynamics Calculation Unit: Based on the Saint-Venant equations, the flood evolution process is solved, and the model parameters are optimized through the gradient descent algorithm to generate flood evolution data; This unit first receives standardized multi-source meteorological data from the data preprocessing module as input parameters. The Saint-Venant equations are the core equations describing one-dimensional unsteady flow, including the continuity equation and the momentum equation, used to simulate the evolution dynamics of floods in river channels and floodplains. The continuity equation ensures mass conservation, and the formula is:

[0032] in, Indicates water depth. Indicates flow rate, For time, For spatial coordinates, The sign of the partial derivative. For unit width flow, The rate of change of water depth per unit time. The rate of change of unit width flow along the river channel; The momentum equation, on the other hand, takes into account inertia, gravity, friction, and pressure terms, and the formula is:

[0033] in It is the acceleration due to gravity. For the riverbed slope, For friction slope, For local acceleration, For convective acceleration, For stress terms, Indicates water depth. Indicates flow rate, For time, For spatial coordinates, The sign for partial derivatives; The solution process is discretized, and the spatiotemporal distribution of flood inundation depth and extent is obtained by combining initial and boundary conditions. To improve simulation accuracy, the unit integrates gradient descent algorithm to optimize model parameters. Its core principle is to minimize the loss function between simulated and measured values. Specifically, the loss function is defined as follows:

[0034] in, Indicates the parameters to be optimized. For the first Simulated values ​​for each sample For the first Measured values ​​from ground monitoring stations for each sample. For the number of data points, For loss function, For indexing; The gradient descent iterative formula is:

[0035] in, For learning rate, loss function exist gradient at this point The old value of the parameter. For the new value of the parameter, Indicates the parameters to be optimized. The loss function; By calculating gradients and updating parameters until convergence to the optimal solution, the unit finally outputs calibrated evolution data.

[0036] M23, Risk Assessment Unit: Used to calculate the depth and extent of flood inundation using probabilistic statistical methods, and outputs the risk level as early warning data in combination with population density data.

[0037] M3, a real-time monitoring module, receives early warning data output by the flood evolution simulation module, and is used to collect real-time environmental data, integrate multi-source monitoring information, and output monitoring results; specifically including: M31, Sensor Network Unit: Employs a distributed IoT sensor array, deployed at key nodes in the river channel, to collect real-time environmental data through water level sensors, flow velocity sensors, and rainfall sensors; M32, Data Fusion Unit: Employs a Kalman filter algorithm to integrate real-time environmental data collected by multiple sensors, generating fused environmental information; the specific implementation method is as follows: The data fusion unit first defines the system's state vector. For flood monitoring applications, the state vector... Typically including key hydrological variables, the unit's execution process consists of two core steps: Prediction phase: Based on the optimal estimate of the previous time step, the dynamic model of the system is used to predict the state and uncertainties at the current time step; State prediction: Prediction is made based on a simple physical model, using the following formula:

[0038] in, yes Prior state estimation at time 10:00 It is the state transition matrix. It is the posterior state estimate at time k-1. It's process noise. For indexing, Indicates prior estimation; Covariance prediction: Simultaneously updating the uncertainty of the state estimate, the formula is:

[0039] in, It is a priori estimate of covariance. It is the state transition matrix. It is the covariance estimated in the posterior time step of the previous time step. It is the process noise covariance matrix. It is the transpose operator. Indicates prior estimation, This represents the posterior estimate; Update phase: When When new observation data arrives at a given time, the predicted value is weighted and fused with the observed value to obtain the optimal estimate; Calculating the Kalman gain: This is the core of the algorithm, determining whether we place more trust in the predictive model or in the current sensor observations. The formula is:

[0040] in, It is the Kalman gain matrix. It is a priori estimate of covariance. It is the observation matrix. It is the observation noise covariance matrix. It is the transpose operator. Indicates prior estimation; When the sensor measurement is very accurate, the Kalman gain matrix The larger the value, the more the algorithm tends to trust the observed values; conversely, when the model's predictions are very accurate, it tends to trust the predicted values ​​more. State update: The predicted and observed values ​​are fused using Kalman gain to obtain the optimal estimate for the current time step;

[0041] in, yes The posterior state estimate sought at each time step. yes Prior state estimation at time 10:00 It is the Kalman gain matrix. yes The actual observations of the time sensor It is the observation matrix. The difference between observed and predicted values, Indicates prior estimation; Covariance Update: Finally, update the uncertainty of the optimal estimate;

[0042] in, It is a posterior estimate of covariance. It is the identity matrix. It is the Kalman gain matrix. It is the observation matrix. It is a priori estimate of covariance. Indicates prior estimation, This represents the posterior estimate; Through this recursive process, the data fusion unit can dynamically and adaptively weigh the reliability of physical model predictions and multi-sensor measured data, smooth random fluctuations, eliminate abnormal noise, and finally output continuous, stable and highly confident fused environmental information.

[0043] M33, Anomaly Detection Unit: Based on the isolated forest algorithm, it identifies data anomalies in the fused environmental information and outputs the anomaly monitoring results to the early warning release module; The core idea of ​​the Isolation Forest algorithm is that "outliers are more likely to be isolated," which means that by constructing multiple random trees, data points that deviate from the normal pattern can be quickly identified. The implementation process mainly includes two stages: offline training and online detection. During the offline training phase, each unit randomly selects subsamples from historical normal data to construct multiple isolated trees. The generation of each tree uses a recursive random partitioning method. A feature is randomly selected, and a split point is randomly chosen within the range of the feature's maximum and minimum values ​​to divide the data into left and right subtrees. This process continues until all samples are isolated and the maximum depth of the trees is reached. This process eliminates the need for distance and density calculations, improving efficiency. The algorithm enhances robustness by integrating the isolation capabilities of multiple trees. A key formula is used to calculate the anomaly score of a sample, and its expression is:

[0044] in, Indicates abnormal scores. For the data points to be detected, For mathematical expectation, It is a normalization factor. The number of subsamples. For path length, This represents the average path length. The closer the score is to 1, the more likely the sample is to be abnormal. A score much less than 0.5 indicates that the sample is more likely to be normal. During the online detection phase, the unit will sequentially pass the sensor data that comes in in real time through the pre-trained isolated forest. For each new data point, the path length in each tree is calculated and the average path length and abnormal score are summed. When the score exceeds the preset threshold (0.7), it is judged as abnormal and an early warning signal is triggered. Otherwise, it is regarded as normal data and monitoring continues.

[0045] M4, the early warning release module, receives the monitoring results output by the real-time monitoring module and is used to generate early warning instructions, release early warning information, and adjust early warning parameters; specifically including: M41, Warning Level Classification Unit: Used to classify warning instructions into three levels: red, orange, and yellow, according to the flood risk level classification standard; M42, Communication Unit: Used to release early warning information through dual channels of 5G network and satellite communication; M43, Feedback Mechanism Unit: Used to receive user feedback data, analyze warning-related data through machine learning algorithms, and dynamically adjust warning parameters to optimize response time.

[0046] In the data preprocessing stage, the unit cleans, aligns, and performs feature engineering on multi-source heterogeneous data to construct feature vectors. Subsequently, the unit trains a lightweight and highly interpretable machine learning model using logistic regression to establish a mapping relationship between features and effects. By learning patterns in historical data, the model can quantitatively evaluate the effectiveness of a new warning. The core of this model is the sigmoid function, whose formula is:

[0047] in Represents probability. This represents the target variable that we want to predict. This represents the input feature vector. It is the weight vector learned by the model. It is a bias term. It is a natural constant. It is the transpose operator; The goal of model training is to find the optimal... and This ensures that the predicted probability best matches the actual effect. During the deployment phase, for each newly issued warning, the feedback mechanism unit extracts its feature vector in real time and inputs it into the trained model to calculate the probability of it being "accurate", "missed", or "false". When the probability of "false" or "missed" exceeds the preset threshold, the warning effect is deemed poor.

[0048] M5, an adaptive optimization module, receives early warning data output by the flood evolution simulation module, and is used to evaluate the model simulation accuracy, fine-tune model parameters, and generate model optimization instructions; specifically including: M51, Model Optimization Unit: Employs Bayesian optimization methods to periodically evaluate the prediction accuracy of deep learning models; M52, Parameter Adjustment Unit: Through sensitivity analysis, key parameters affecting simulation accuracy are identified, and parameters of the hydrodynamic calculation unit are optimized. M53, Algorithm Update Unit: Used to trigger the model retraining process when model drift is detected, and to generate model optimization instructions using new data to maintain the platform's adaptability.

[0049] M6, the report generation module, integrates the aforementioned early warning data and monitoring results to generate flood risk visualization results and output a flood risk assessment report; specifically including: M61, Visualization Unit: Generates interactive risk maps based on WebGL technology, supports three-dimensional flood inundation simulation, and forms flood risk visualization results; M62, Report Generation Unit: Used to integrate historical flood data with real-time monitoring results and automatically generate a structured flood risk assessment report using natural language processing technology. The assessment report includes flood evolution trends, risk distribution maps, and emergency recommendations.

[0050] M7, a data preprocessing module, which is connected between the data acquisition module and the flood evolution simulation module, specifically includes: M71, Data Cleaning Unit: Uses box plot method to identify outliers in the raw data and remove them; M72, Standardized Unit: The Z-score normalization algorithm is used to perform scale unification on the cleaned data, and the standardized data is output to the flood evolution simulation module to ensure data quality. The core formula of its normalization process is:

[0051] in, The original numerical value representing a certain feature. This represents the average value of the feature across all samples. The standard deviation of this feature. This is the normalized output value; During implementation, the unit operates in two phases: the offline training phase, which uses historical flood event data to calculate the global mean and standard deviation of each feature as benchmark parameters; and the online processing phase, which applies the above formula to the real-time incoming sensor data stream for rapid transformation, mapping the original values ​​to a uniform scale. At the same time, the unit will periodically update the statistical parameters to adapt to data distribution drift, ensuring the reliability of the normalization effect in long-term operation.

[0052] M8, the user interaction module, receives user-input query commands, displays risk maps and early warning information through a graphical user interface unit, and supports custom simulation parameters; specifically including: M81 is a graphical user interface unit used to receive user-input query commands, display flood risk visualization results and early warning information, and support user-defined flood simulation parameters. It can also adjust the flood evolution model input based on the user-defined parameters and update the simulation results in real time.

[0053] In addition, the platform integrates blockchain technology and is equipped with a distributed ledger unit. The distributed ledger unit is used for secure data storage and records all data operation logs of the platform. Furthermore, the distributed ledger unit is linked with the report generation module to provide audit trail functionality for flood risk assessment reports.

[0054] Example 2 This implementation outlines the operational steps of a deep learning-based extreme climate flood evolution simulation and risk early warning platform, specifically including: S1. Multi-source data acquisition and fusion preprocessing: After the platform is launched, the data acquisition module first acquires a wide range of heterogeneous data from multiple sources. Specifically, the satellite remote sensing data analysis unit extracts large-scale precipitation distribution information through multispectral imaging technology to generate remote sensing precipitation data. At the same time, the ground monitoring station data integration unit synchronously receives real-time ground measurement data transmitted from sensor arrays deployed at key nodes in the river channel. Subsequently, the unit uses a calibration algorithm based on linear regression to correct the bias of the remote sensing data and fuses the corrected data with the ground measurement data to generate a high-quality basic dataset that is consistent in time and space. Meanwhile, the data preprocessing module is launched. Its data cleaning unit uses box plot method to identify and remove outliers in the original data. The standardization unit follows closely behind, applying the Z-score normalization algorithm to unify the dimensions of all features, thereby providing clean and standardized input for subsequent model calculations and ensuring data quality.

[0055] S2. High-precision simulation of flood evolution coupled with deep learning and physical models: The preprocessed basic data is fed into the core of the flood evolution simulation module—the deep learning model unit. This unit adopts an architecture that integrates convolutional neural networks and long short-term memory networks. The CNN is dedicated to extracting topographic elevation and river morphology spatial features, while the LSTM is adept at modeling precipitation trends and the time series dependence of flood propagation delay. The two work together to adaptively learn nonlinear hydrological effects under extreme climate conditions. To ensure the physical realism of the simulation, the hydrodynamic calculation unit solves the core equations of fluid mechanics, namely the Saint-Venant equations. The continuity equation ensures the conservation of mass, and the momentum equation describes the hydrodynamic process. This unit also integrates a gradient descent optimization algorithm to dynamically adjust key parameters by minimizing the loss function, ultimately generating high-precision flood evolution data.

[0056] S3. Real-time monitoring and anomaly identification based on sensor networks and intelligent algorithms: After the flood evolution data triggers the early warning, the real-time monitoring module enters a highly active state. The distributed IoT sensor array continuously collects real-time data streams of the river environment. The data fusion unit uses the Kalman filter algorithm to integrate and process this data stream. Through recursive calculations in the prediction and update phases, it eliminates noise and generates stable and reliable fused environmental information. Then, the anomaly detection unit scans the fused information based on the isolated forest algorithm. This algorithm determines whether the data points deviate from the normal pattern by calculating the anomaly score. Once the score exceeds the threshold, it indicates that a sudden flood has been detected, and the monitoring results are immediately output.

[0057] Step 4: Closed-loop control of multi-channel early warning issuance and effect feedback: After receiving the abnormal monitoring results, the early warning release module classifies the warning into three levels: red, orange, and yellow, based on preset risk standards. The communication unit then quickly and reliably releases the warning information to relevant emergency departments and the public through a dual-channel system consisting of 5G and satellite communication. At the same time, the feedback mechanism unit starts working. It receives user feedback data on the warning effect and uses machine learning algorithms such as logistic regression to analyze the probability of the warning being "accurate," "missed," or "false." It then dynamically adjusts the warning parameters to form a closed-loop optimization mechanism to optimize the response time and accuracy of subsequent warnings.

[0058] S4. Visualization of Model Self-Optimization and Risk Assessment Report Generation The platform's adaptive optimization module runs continuously in the background to ensure its long-term effectiveness. The model optimization unit uses Bayesian optimization methods to periodically evaluate the predictive accuracy of the deep learning model, while the parameter adjustment unit identifies model weaknesses through sensitivity analysis. Once the algorithm update unit detects model performance drift, it automatically triggers a retraining process, generating optimization instructions using new data to enable the platform to continuously learn. Finally, the report generation module integrates early warning data and monitoring results from the entire process. The visualization unit generates interactive 3D risk maps based on WebGL technology, and the report generation unit automatically generates structured assessment reports containing flood evolution trends, risk distribution maps, and emergency recommendations using natural language processing technology. This provides decision-makers with intuitive and comprehensive decision support. All key data operations are stored using blockchain technology to ensure the reliability and auditability of the assessment reports.

[0059] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A deep learning-based extreme climate flood evolution simulation and risk warning platform, characterized in that, include: The data acquisition module is used to acquire multi-source meteorological data, geographical parameters, and basic data related to historical floods; The flood evolution simulation module receives the basic data output by the data acquisition module and is used to perform flood simulation operations, generate flood evolution data, and output early warning data. The real-time monitoring module receives the early warning data output by the flood evolution simulation module, and is used to collect real-time environmental data, integrate multi-source monitoring information, and output monitoring results. The early warning release module receives the monitoring results output by the real-time monitoring module and is used to generate early warning instructions, release early warning information, and adjust early warning parameters. The adaptive optimization module receives the early warning data output by the flood evolution simulation module, and uses it to evaluate the model simulation accuracy, fine-tune the model parameters, and generate model optimization instructions. The report generation module integrates the early warning data and monitoring results to generate flood risk visualization results and output a flood risk assessment report.

2. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The data acquisition module includes: Meteorological data acquisition unit: used to acquire multi-source meteorological data; Terrain data integration unit: used to set geographic parameters; Historical flood database unit: used to store flood evolution path data under historical extreme climate events, and compare it with current meteorological data through a spatiotemporal matching unit; the spatiotemporal matching unit is used to match the currently monitored meteorological data with the event data in the historical flood database; The satellite remote sensing data parsing unit is used to extract precipitation distribution information through multispectral imaging technology and generate remote sensing precipitation data; The ground monitoring station data integration unit is used to receive the remote sensing precipitation data, combine it with the measured data collected by the rain gauge and the flow velocity sensor, and output calibrated multi-source meteorological data through the data calibration algorithm.

3. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The flood evolution simulation module includes: Deep learning model unit: It adopts an architecture that integrates convolutional neural networks and long short-term memory networks. The convolutional neural network is used to extract spatial features of terrain elevation and river morphology, and the long short-term memory network is used to model the time series dependence of precipitation trends and flood propagation delay. Hydrodynamics calculation unit: Based on the Saint-Venant equations, it solves the flood evolution process and optimizes the model parameters through the gradient descent algorithm to generate flood evolution data; Risk assessment unit: Used to calculate the depth and extent of flood inundation using probabilistic statistical methods, and outputs a risk level as early warning data in combination with population density data.

4. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The real-time monitoring module includes: Sensor Network Unit: Employs a distributed IoT sensor array deployed at key nodes in the river channel to collect real-time environmental data through water level sensors, flow velocity sensors, and rainfall sensors; Data fusion unit: It uses the Kalman filter algorithm to integrate real-time environmental data collected by multiple sensors and generate fused environmental information; Anomaly detection unit: Based on the isolated forest algorithm, it identifies data anomalies in the fused environmental information and outputs anomaly monitoring results.

5. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The early warning release module includes: Warning level classification unit: used to classify warning instructions into three levels: red, orange, and yellow, according to the flood risk level classification standard; Communication unit: Used to issue early warning information through dual channels of 5G network and satellite communication; Feedback mechanism unit: Used to receive user feedback data, analyze warning-related data through machine learning algorithms, and dynamically adjust warning parameters.

6. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The adaptive optimization module includes: Model optimization unit: Using Bayesian optimization methods, the prediction accuracy of the deep learning model is evaluated periodically; Parameter tuning unit: Through sensitivity analysis, key parameters affecting simulation accuracy are identified, and parameters of the hydrodynamic calculation unit are optimized. Algorithm update unit: used to trigger the model retraining process when model drift is detected, and to generate model optimization instructions using the new data.

7. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The report generation module includes: Visualization Unit: Generates interactive risk maps based on WebGL technology, supports 3D flood inundation simulation, and produces flood risk visualization results; Report generation unit: Used to integrate historical flood data with real-time monitoring results, and automatically generate a structured flood risk assessment report through natural language processing technology. The assessment report includes flood evolution trend, risk distribution map and emergency recommendations.

8. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, It also includes a data preprocessing module, which is connected between the data acquisition module and the flood evolution simulation module, and specifically includes: Data cleaning unit: Uses box plot method to identify outliers in the raw data and remove them; Standardization Unit: The Z-score normalization algorithm is used to perform scale unification processing on the cleaned data, and the standardized data is output to the flood evolution simulation module.

9. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, It also includes a user interaction module, which includes: The graphical user interface unit is used to receive user-input query commands, display flood risk visualization results and early warning information, and supports user-defined flood simulation parameters. It can also adjust the flood evolution model input based on the user-defined parameters and update the simulation results in real time.

10. The deep learning-based extreme climate flood evolution simulation and risk early warning platform according to claim 1, characterized in that, The platform integrates blockchain technology and is equipped with a distributed ledger unit. The distributed ledger unit is used for secure data storage and records all data operation logs of the platform. Furthermore, the distributed ledger unit is linked with the report generation module to provide audit trail functionality for flood risk assessment reports.

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