Rapid risk assessment system for hydrogeological disasters

Abstract

The invention discloses a rapid risk assessment system for hydrogeological disasters, and relates to the field of natural disaster risk management, and the system comprises a data collection and preprocessing module which is used for collecting multi-source original data and carrying out the parallelization preprocessing; the feature extraction module is used for extracting hysteresis, stage and reactivation features of the landslide from the preprocessed data; the feature fusion module is used for weighting and standardizing the extracted features; and the disaster risk assessment module is used for carrying out risk assessment by adopting a regression analysis model based on the fused feature data and generating an emergency response strategy. Through multi-source data fusion and intelligent model analysis, more accurate, more comprehensive and more timely landslide risk dynamic assessment and early warning are realized, so that improvement of disaster prevention and reduction capabilities is facilitated, and disaster loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of natural disaster risk management, and in particular to a rapid risk assessment system for hydrogeological disasters. Background Technology

[0002] Hydrogeological hazards, especially landslides and debris flows, are often triggered by multiple factors such as precipitation, geological structure changes, and land use changes, causing serious impacts on the natural environment and society. Traditional hydrogeological hazard risk assessment methods mostly rely on geological surveys, historical disaster data, and human experience, often limited to the use of a single data source. The assessment results are limited by long data collection times, low accuracy, and strong localization. These traditional methods cannot quickly and accurately address complex hydrogeological hazard risks, especially when facing frequently changing environmental conditions, where the response capability and accuracy of disaster early warning are insufficient.

[0003] Although remote sensing, big data analytics, and artificial intelligence (AI) have been gradually applied to hydrogeological hazard assessment in recent years, existing technologies still face many challenges. Current hazard assessment models often rely on static data sources, making them ineffective at handling the fusion of multi-source data. Various types of data (such as hydrological, meteorological, and geological data) differ in origin, format, volume, and timeliness, making it difficult for existing technologies to meet the needs of rapid assessment and real-time early warning. While multi-source data fusion technologies have received some research, they often suffer from insufficient data standardization, inadequate model accuracy, and low processing efficiency, hindering their ability to provide efficient support for hazard early warning.

[0004] To address the aforementioned problems, this invention provides a rapid risk assessment system for hydrogeological disasters. By combining multi-source data fusion and rapid feature extraction technology, it can quickly and accurately assess the risks of hydrogeological disasters. Summary of the Invention

[0005] To address the above problems, this invention provides a rapid risk assessment system for hydrogeological disasters, which solves the problems of insufficient multi-source data fusion, low assessment accuracy, and slow response speed in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a rapid risk assessment system for hydrogeological hazards, comprising:

[0007] The data acquisition and preprocessing module is used to acquire raw data from multiple sources and perform parallel preprocessing.

[0008] The feature extraction module is used to extract the hysteresis, stage and reactivation features of landslides from the preprocessed data;

[0009] The feature fusion module is used to perform weighted and standardized processing on the extracted features;

[0010] The disaster risk assessment module uses a regression analysis model to assess risks and generate emergency response strategies based on the fused feature data.

[0011] Furthermore, the data acquisition and preprocessing module includes a raw data acquisition unit and a parallel data processing unit. The raw data acquisition unit is used to acquire the following raw data:

[0012] Hydrological data: Rainfall, flow velocity, and groundwater level data are collected in real time through hydrological monitoring stations and meteorological stations, serving as the hydrological data stream;

[0013] Geological data: Soil type, stratigraphic information, lithology and slope data are collected by the Geological Survey Bureau to form a geological data stream;

[0014] Meteorological data: Temperature, humidity, and wind speed data are collected through a meteorological data interface to form a meteorological data stream;

[0015] Remote sensing data: Image data of land surface morphology and temperature, as well as NDVI data, are collected by remote sensing satellites to form a remote sensing data stream;

[0016] Socioeconomic data: Socioeconomic data streams are formed by obtaining information on population density, infrastructure, and economic activity from public government platforms and databases.

[0017] Furthermore, the data parallel processing unit is used to process the acquired raw data in parallel, and the specific operations are as follows:

[0018] Parallel Flow 1: Hydrological data are uniformly converted to a standard format and all hydrological data are converted to the WGS84 coordinate system; time series interpolation is used to process missing data in the hydrological data; outliers in the hydrological data are removed using the standard deviation method; precipitation data is uniformly converted to mm / h and flow velocity data is uniformly converted to m / s;

[0019] Parallel Stream 2: Formats and converts meteorological data into a unified coordinate system; performs spatial interpolation on the meteorological data to fill in missing values; uses the IQR method to remove outliers from the meteorological data; unifies temperature to °C and humidity to percentage.

[0020] Parallel Stream 3: Converts the format of remote sensing data and performs spatial coordinate transformation on the image data; fills in missing pixels in the remote sensing data using Kriging interpolation; uses image restoration techniques to detect and remove outliers in the remote sensing data, and normalizes the NDVI data to the range of [0,1].

[0021] Furthermore, the feature extraction module includes:

[0022] The lag feature extraction unit establishes a lag period model based on historical landslide events and corresponding precipitation and meteorological data. It calculates the landslide lag period through time series analysis and extracts lag features, including the duration of the lag period, precipitation intensity, and precipitation frequency.

[0023] The phased feature extraction unit identifies landslide deformation stages based on geological and deformation data. The deformation stages include the initial creep stage and the accelerated deformation stage, and a higher risk weight is assigned to the accelerated deformation stage. The geological data includes soil type and lithology, and the deformation data includes surface displacement and settlement.

[0024] The resurgence feature extraction unit analyzes the possibility of landslide recurrence based on historical landslide data, external disturbance data, and the stability recovery process of the landslide area. It compares the time intervals of historical recurrence events with external disturbance factors, establishes a resurgence risk model, and extracts resurgence features.

[0025] Furthermore, the feature fusion module includes:

[0026] The feature weighted fusion unit assigns weights to lag, stage, and resurrection features based on historical data, expert experience, or principal component analysis. The weights of lag and stage features are higher than those of resurrection features, generating a weighted multi-dimensional feature set.

[0027] The feature standardization unit is used to standardize the multi-dimensional feature set after weighted fusion. It uses the z-score standardization method to unify the dimensions of the weighted feature set and outputs multi-dimensional feature data.

[0028] Furthermore, the disaster risk assessment module includes:

[0029] The model training and construction unit utilizes historical disaster data and multi-dimensional feature data from the feature standardization unit to train the model through regression analysis, learn the relationship between disasters and features, and construct a disaster risk assessment model for landslide risk prediction.

[0030] The risk assessment and prediction unit uses a trained regression analysis model to assess the landslide risk in different areas and outputs the risk level and corresponding emergency response strategy. The risk level includes high risk, medium risk and low risk. The emergency response strategy includes emergency evacuation or engineering reinforcement strategy for high-risk areas, enhanced monitoring strategy for medium-risk areas, and routine maintenance and monitoring strategy for low-risk areas.

[0031] The risk analysis and optimization unit compares the risk assessment results with the actual disaster occurrence, adjusts the parameters of the regression analysis model, introduces new features to achieve optimization, and adjusts the emergency response strategy.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] This invention combines multi-source datasets and feature extraction techniques, and uses a regression analysis model to assess and predict landslide risks. By training the model to learn the relationship between different disaster characteristics and landslide risks, it can achieve accurate prediction of disaster occurrences, greatly improving the accuracy of disaster prediction.

[0034] This invention integrates features from multiple data sources, including hydrology, meteorology, geology, and remote sensing, and fully utilizes the complementarity of different features to form a multi-dimensional feature set. These comprehensive features provide more comprehensive data support for disaster risk assessment and enhance the model's overall understanding of landslide risk.

[0035] This invention uses real-time updated multi-dimensional feature data to dynamically assess landslide risks in different regions. Based on the model's predictions, the regions are classified into high, medium, and low risk levels, and corresponding emergency response and early warning measures are provided to help relevant departments take timely preventative measures and reduce losses caused by disasters. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is the system architecture diagram of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but is merely a selection of embodiments of the present invention.

[0039] Reference Figure 1 A rapid risk assessment system for hydrogeological hazards, comprising:

[0040] The data acquisition and preprocessing module performs parallel processing on the acquired raw data, completing data standardization, missing value imputation, outlier removal, and unit normalization operations, converting it into a multi-source dataset in a unified format.

[0041] The data acquisition and preprocessing module includes: a raw data acquisition unit and a parallel data processing unit;

[0042] The raw data acquisition unit is responsible for collecting raw data related to hydrogeological hazards from multiple data sources. This raw data includes hydrological data, geological data, meteorological data, remote sensing data, and socioeconomic data. The data acquisition methods are as follows:

[0043] Hydrological data is collected in real time from hydrological monitoring stations and meteorological stations, including precipitation, flow velocity, and groundwater level data, forming a hydrological data stream. Geological data is collected from geological survey bureaus, including soil type, stratigraphic information, lithology, and slope data, forming a geological data stream. Meteorological data is collected from meteorological data interfaces, including temperature, humidity, and wind speed data, forming a meteorological data stream. Remote sensing data is collected from remote sensing satellites, including image data of land surface morphology, normalized difference vegetation index (NDVI), and surface temperature, forming a remote sensing data stream. Socioeconomic data is obtained from publicly available government platforms and databases, including information on population density, infrastructure, and economic activity, forming a socioeconomic data stream.

[0044] The data parallel processing unit is responsible for processing the collected raw data in parallel, and performs data format standardization, missing value imputation, outlier removal and unit normalization operations for different data types.

[0045] Data format standardization is performed as follows:

[0046] Parallel Stream 1 unifies hydrological data into a standard format and unifies all hydrological data into the WGS84 coordinate system;

[0047] Parallel Stream 2 formats and converts meteorological data into a unified coordinate system;

[0048] Parallel Stream 3 converts the remote sensing data format and performs spatial coordinate transformation on the image data.

[0049] Missing value imputation is performed as follows:

[0050] Parallel Stream 1 uses time series interpolation to process missing hydrological data;

[0051] Parallel Stream 2 performs spatial interpolation on meteorological data to fill in missing values;

[0052] Parallel Stream 3 fills in missing pixels in remote sensing data using Kriging interpolation.

[0053] Outlier removal is performed as follows:

[0054] Parallel Stream 1 uses the standard deviation method to remove outliers from hydrological data;

[0055] Parallel Stream 2 uses the IQR method to remove outliers from meteorological data;

[0056] Parallel Stream 3 uses image restoration techniques to detect and remove outliers in remote sensing data.

[0057] Unit normalization is performed as follows:

[0058] Parallel flow 1, with a uniform precipitation of mm / h and a flow velocity of m / s;

[0059] Parallel flow 2, with a uniform temperature of ℃ and humidity as a percentage;

[0060] Parallel Stream 3 normalizes NDVI data to the range [0,1].

[0061] Through the parallel processing described above, each data type underwent format standardization, missing value imputation, outlier removal, and unit normalization according to its own characteristics, and finally the processed data streams were integrated into a multi-source dataset.

[0062] It should be noted that in the original data acquisition unit, the data sources include multiple platforms and monitoring stations. These data are usually collected in different formats and at different frequencies: hydrological data comes from hydrological monitoring stations and is collected at a relatively high frequency, usually on an hourly basis; while geological data mainly comes from the geological survey bureau and is updated at a relatively low frequency, usually annual or quarterly data.

[0063] Since each data source has different data formats, collection frequencies, and processing methods, sequential processing would not only increase processing time but may also affect the real-time performance of the processing. Therefore, by using a parallel stream design, preprocessing of multiple data sources can be performed simultaneously, with each data source being processed in an independent process, thereby greatly shortening the overall data processing time.

[0064] Hydrological data, meteorological data, and remote sensing data are different types of data, and their processing methods and data characteristics are different. Meteorological data usually requires spatial interpolation, while remote sensing data requires image restoration technology. Using parallel streaming allows each data type to be processed independently according to its specific requirements, avoiding interference and influence between data sources.

[0065] Furthermore, if more data sources need to be introduced in the future, only a separate processing method needs to be designed for the new data streams and incorporated into the parallel processing system, without changing other parts of the existing process.

[0066] The feature extraction module is responsible for extracting features related to hydrogeological hazards from multi-source datasets. By extracting different features, it obtains the lag, stage and reactivation features of landslides.

[0067] The feature extraction module includes: a lag feature extraction unit, a staged feature extraction unit, and a reactivation feature extraction unit;

[0068] The lag feature extraction unit is responsible for extracting lag features of landslides from multi-source datasets and analyzing the relationship between the lag time affecting landslide occurrence and influencing factors.

[0069] By analyzing historical landslide events and their corresponding precipitation and meteorological conditions, a lag model is established. Based on time series data, the lag period of landslide occurrence is calculated, i.e. how long it takes for factors such as precipitation and meteorological conditions to trigger a landslide event. The lag characteristics are extracted through the lag model, including the duration of the lag period, precipitation intensity, and precipitation frequency.

[0070] The stage feature extraction unit is responsible for extracting the stage features of the landslide from the multi-source dataset, identifying which deformation stage the landslide is in, and adjusting the risk assessment value of the landslide according to the stage.

[0071] By collecting geological and deformation data of the landslide area, the deformation stages of the landslide are identified. Different deformation stages correspond to different risk levels. Based on the deformation records and geological characteristics of historical landslides, stage-specific characteristic data of landslides in different deformation stages are extracted. For landslides in the accelerated deformation stage, a higher risk weight is assigned.

[0072] Among them, geological data includes soil type and lithology, deformation data includes surface displacement and settlement, and deformation stages include initial creep stage and accelerated deformation stage;

[0073] The resurgence feature extraction unit is responsible for extracting resurgence features of landslides from multi-source datasets, analyzing the recurrence risk after a landslide occurs, and the impact of external disturbance factors on recurrence.

[0074] By collecting historical landslide data, external disturbance data, and the stability recovery process of landslide areas, we analyze whether there is a possibility of landslide recurrence. By comparing the time intervals of historical recurrence events and external disturbance factors, we establish a reactivation risk model. For landslides that have already occurred, we analyze whether they are in a high-risk period of reactivation and extract relevant recurrence characteristics, namely reactivation characteristic data.

[0075] It should be noted that in the feature extraction process, the extraction methods for the lag, stage and reactivation features of landslides mainly rely on the analysis of time series data. The lag feature extraction calculates the lag period by the relationship between the occurrence time of historical landslide events and meteorological data. This process is based on time series analysis methods.

[0076] The feature fusion module performs weighted and standardized processing on the features extracted from the feature extraction module to generate a multi-dimensional, standardized feature set, which serves as the input to the disaster risk assessment model.

[0077] The feature fusion module includes: a feature weighted fusion unit and a feature normalization unit;

[0078] The feature weighted fusion unit is responsible for weighting and fusing features extracted from different feature extraction units to generate a comprehensive feature set. During the fusion process, the weight of each feature is adjusted according to its impact on the landslide risk assessment.

[0079] Features from different sources, such as lag, phase, and reactivation, are merged to form a comprehensive feature set. Each feature is assigned a weight based on its importance in landslide risk assessment. The weights can be set based on historical data analysis, expert experience, or principal component analysis (PCA). Through weighting, the values ​​of each feature are multiplied by their corresponding weights to generate a fused multidimensional feature set. This feature set can better highlight features that have a high impact on landslide risk prediction.

[0080] The feature standardization unit is responsible for standardizing the multi-dimensional feature set after weighted fusion, ensuring that the dimensions and ranges of all features are consistent, and avoiding the influence of some features on the model being too large or too small due to differences in dimensions.

[0081] During the standardization process, the multi-dimensional feature set is uniformly standardized using the z-score standardization method, which unifies the value of each feature to the same scale, giving them the same dimensions. The standardized features ensure that all features are compared under the same standard, outputting multi-dimensional feature data and avoiding the unbalanced impact on the model caused by the large range of values ​​of some features.

[0082] It should be noted that in the feature-weighted fusion unit, the allocation of weights needs to be adjusted according to the actual impact of each feature in the landslide risk assessment. Since lagging features and stage features have a greater impact on the assessment of landslide risk, their weights will be higher, while reactivation features mainly affect the recurrence risk of landslides that have already occurred, and their weights are relatively lower.

[0083] The disaster risk assessment module, based on the multi-dimensional feature data output by the feature fusion module, uses a regression analysis model to assess and predict disaster risks. By analyzing the landslide risk levels in different regions, the model generates corresponding emergency response strategies and early warning information.

[0084] The disaster risk assessment module includes: a model training and construction unit, a risk assessment and prediction unit, and a risk analysis and optimization unit.

[0085] The model training and construction unit utilizes multi-dimensional feature data and historical disaster data from the feature standardization unit to train the model through regression analysis, learn the relationship between disasters and features, and finally construct a disaster risk assessment model for landslide risk prediction.

[0086] During the model training phase, historical disaster data and multi-dimensional feature data from feature standardization units are input into the regression analysis model. The regression analysis model will begin to learn the relationship between disaster features (such as precipitation, slope, and land use) and landslide occurrence. Through training, the model optimizes its parameters until it can accurately predict the risk of disaster occurrence. After training, the model will have the ability to assess landslide risk based on feature data.

[0087] The risk assessment and prediction unit uses a pre-trained regression analysis model to assess the landslide risk in different regions, output the risk level, and provide decision-makers with corresponding emergency response suggestions.

[0088] After the regression analysis model is trained, the risk assessment and prediction unit receives multi-dimensional feature data from the feature fusion module and inputs it into the trained regression analysis model. The model calculates the probability of landslide occurrence for each region based on the input feature data and outputs the risk level of the region: high risk, medium risk, and low risk. Based on the assessment results, emergency response strategies and early warning information are generated to help decision-makers formulate disaster prevention and mitigation measures.

[0089] The Risk Analysis and Optimization Unit is responsible for analyzing the effectiveness of risk assessment results and optimizing them. By optimizing the regression analysis model, it improves the accuracy and effectiveness of the assessment results.

[0090] By comparing the risk assessment results output by the model with the actual disaster occurrence, the accuracy of the model's predictions is analyzed. If there is a significant deviation between the risk assessment and the actual disaster occurrence in certain areas, the risk analysis and optimization unit will fine-tune the model by adjusting the parameters of the regression analysis model and introducing new features to optimize the model's performance. Based on the optimized assessment results, emergency response strategies are adjusted to ensure that these strategies are more in line with actual needs and to formulate more precise emergency response measures for different risk levels.

[0091] It should be noted that in the model training and construction unit, the input data used by the regression analysis model includes multi-dimensional feature data from the feature fusion module. By training the model, it is possible to learn the relationship between features and disaster risk based on historical disaster data and feature data, thereby achieving accurate prediction of landslide risk in practical applications.

[0092] Regression analysis models, trained on historical disaster data, learn relationships that go beyond mathematical formulas or parameters. They also include the correlations between various features and disaster risks. These relationships can be expressed as follows: the model associates each input feature with the probability or intensity of a landslide, expressing the relationship through a formula. After training, the model can predict the probability of a landslide based on new input features. These predictions, based on the relationships between features and risks learned during training, can output a landslide risk score for each region or time period.

[0093] The emergency response strategy of the risk assessment and prediction unit is automatically generated based on the predicted risk level. For high-risk areas, the system will recommend emergency evacuation or reinforcement of protective facilities; for medium-risk areas, it will recommend monitoring and regular inspections; and for low-risk areas, it will recommend routine maintenance and monitoring. The response strategy for each area is customized according to its specific landslide risk level.

[0094] Risk analysis and optimization not only rely on model tuning, but also take into account changes in external factors, such as weather conditions and soil moisture. Therefore, the optimization process is a continuous process that can dynamically adjust model parameters based on new data and assessment results to ensure that disaster risk assessment can adapt to different geographical environments and changing weather conditions.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations will be apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rapid risk assessment system for hydrogeological hazards, characterized in that, include: The data acquisition and preprocessing module is used to acquire raw data from multiple sources and perform parallel preprocessing. The feature extraction module is used to extract the hysteresis, stage and reactivation features of landslides from the preprocessed data; The feature fusion module is used to perform weighted and standardized processing on the extracted features; The disaster risk assessment module uses a regression analysis model to assess risks and generate emergency response strategies based on the fused feature data.

2. The rapid risk assessment system for hydrogeological hazards according to claim 1, characterized in that, The data acquisition and preprocessing module includes a raw data acquisition unit and a parallel data processing unit. The raw data acquisition unit is used to acquire the following raw data: Hydrological data: Rainfall, flow velocity, and groundwater level data are collected in real time through hydrological monitoring stations and meteorological stations, serving as the hydrological data stream; Geological data: Soil type, stratigraphic information, lithology and slope data are collected by the Geological Survey Bureau to form a geological data stream; Meteorological data: Temperature, humidity, and wind speed data are collected through a meteorological data interface to form a meteorological data stream; Remote sensing data: Image data of land surface morphology and temperature, as well as NDVI data, are collected by remote sensing satellites to form a remote sensing data stream; Socioeconomic data: Socioeconomic data streams are formed by obtaining information on population density, infrastructure, and economic activity from public government platforms and databases.

3. The rapid risk assessment system for hydrogeological hazards according to claim 2, characterized in that, The data parallel processing unit is used to process the collected raw data in parallel, and the specific operation is as follows: Parallel Flow 1: Hydrological data are uniformly converted to a standard format and all hydrological data are converted to the WGS84 coordinate system; time series interpolation is used to process missing data in the hydrological data; outliers in the hydrological data are removed using the standard deviation method; precipitation data is uniformly converted to mm / h and flow velocity data is uniformly converted to m / s; Parallel Stream 2: Formats and converts meteorological data into a unified coordinate system; performs spatial interpolation on the meteorological data to fill in missing values; uses the IQR method to remove outliers from the meteorological data; unifies temperature to °C and humidity to percentage. Parallel Stream 3: Converts the format of remote sensing data and performs spatial coordinate transformation on the image data; fills in missing pixels in the remote sensing data using Kriging interpolation; uses image restoration techniques to detect and remove outliers in the remote sensing data, and normalizes the NDVI data to the range of [0,1].

4. The rapid risk assessment system for hydrogeological hazards according to claim 1, characterized in that, The feature extraction module includes: The lag feature extraction unit establishes a lag period model based on historical landslide events and corresponding precipitation and meteorological data. It calculates the landslide lag period through time series analysis and extracts lag features, including the duration of the lag period, precipitation intensity, and precipitation frequency. The phased feature extraction unit identifies landslide deformation stages based on geological and deformation data. The deformation stages include the initial creep stage and the accelerated deformation stage, and a higher risk weight is assigned to the accelerated deformation stage. The geological data includes soil type and lithology, and the deformation data includes surface displacement and settlement. The resurgence feature extraction unit analyzes the possibility of landslide recurrence based on historical landslide data, external disturbance data, and the stability recovery process of the landslide area. It compares the time intervals of historical recurrence events with external disturbance factors, establishes a resurgence risk model, and extracts resurgence features.

5. The rapid risk assessment system for hydrogeological hazards according to claim 1, characterized in that, The feature fusion module includes: The feature weighted fusion unit assigns weights to lag, stage, and resurrection features based on historical data, expert experience, or principal component analysis. The weights of lag and stage features are higher than those of resurrection features, generating a weighted multi-dimensional feature set. The feature standardization unit is used to standardize the multi-dimensional feature set after weighted fusion. It uses the z-score standardization method to unify the dimensions of the weighted feature set and outputs multi-dimensional feature data.

6. The rapid risk assessment system for hydrogeological hazards according to claim 5, characterized in that, The disaster risk assessment module includes: The model training and construction unit utilizes historical disaster data and multi-dimensional feature data from the feature standardization unit to train the model through regression analysis, learn the relationship between disasters and features, and construct a disaster risk assessment model for landslide risk prediction. The risk assessment and prediction unit uses a trained regression analysis model to assess the landslide risk in different areas and outputs the risk level and corresponding emergency response strategy. The risk level includes high risk, medium risk and low risk. The emergency response strategy includes emergency evacuation or engineering reinforcement strategy for high-risk areas, enhanced monitoring strategy for medium-risk areas, and routine maintenance and monitoring strategy for low-risk areas. The risk analysis and optimization unit compares the risk assessment results with the actual disaster occurrence, adjusts the parameters of the regression analysis model, introduces new features to achieve optimization, and adjusts the emergency response strategy.

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