A method and system for predicting casualties caused by earthquake landslides considering population dynamic characteristics and environmental heterogeneity

Abstract

The application discloses a method and system for predicting casualties caused by earthquakes and landslides, taking into account population dynamic characteristics and environmental heterogeneity, which comprises the following steps: step one, collecting all environmental factor data and multi-source population distribution data in the target earthquake area according to a preset spatial unit; step two, inputting the collected environmental factor data into a preset logistic regression model to obtain an earthquake landslide probability; step three, processing the multi-source population distribution data by using a data fusion method to obtain a dynamic population distribution during an earthquake occurrence period; step four, processing the environmental factor data based on a random forest algorithm model and a SHAP method to obtain environmental heterogeneity parameters and environmental factor contribution degrees; and step five, obtaining a probability distribution of casualties caused by earthquake landslides according to a probability prediction model of casualties caused by earthquake landslides. The application improves the accuracy of the evaluation of casualties caused by earthquake landslides and provides a reliable basis for formulating an action plan for earthquake rescue.

Description

Technical Field

[0001] This invention relates to the field of technology, and in particular to a method and system for predicting casualties caused by earthquake landslides, taking into account population dynamics and environmental heterogeneity. Background Technology

[0002] Earthquake-triggered secondary geological disasters manifest in various forms of slope material movement, with their mechanical mechanisms including several typical types: rockfalls, characterized by the instantaneous disintegration of steep rock masses under seismic loads; rockfalls, characterized by the bouncing and rolling of single or multiple rock blocks along a slope; continuous deformation landslides, whose mechanical essence is shear failure on the potential sliding surface; and debris flows with solid-liquid two-phase flow characteristics. Due to their suddenness and cascading effects, these types of disasters have caused significant casualties in numerous strong earthquakes.

[0003] Existing earthquake landslide population loss assessment systems primarily employ statistical methods based on on-site surveys. This approach requires multidisciplinary joint investigation teams to compile disaster data through point-by-point verification. This model reveals significant timeliness deficiencies in earthquake emergency response; the time required from the earthquake's occurrence to completing the verification of all landslide points is substantial, lagging far behind the critical rescue period. Furthermore, factors such as landslide burial depth and rescue progress lead to significant and uncontrollable underreporting and delays in on-site statistics. While Earth observation technology provides new tools for landslide identification, limitations imposed by optical remote sensing satellite revisit cycles, meteorological influences, and data accuracy make it difficult for remote sensing monitoring to meet the real-time requirements of emergency response.

[0004] To meet the timeliness and accuracy requirements of earthquake disaster emergency response, quantitative assessment methods for the number of casualties caused by earthquake landslides have been proposed in recent years. Currently, existing technologies provide a rapid population loss assessment method based on the probability of earthquake landslides. This method first constructs an earthquake landslide probability field based on the Newmark displacement model or machine learning algorithms, and then estimates the population loss caused by earthquake landslides by combining this with population density distribution. This technology reduces the assessment time for earthquake landslide casualties from several days using traditional methods to several hours, providing crucial decision support for emergency rescue and resource allocation.

[0005] However, a comprehensive technical solution for assessing casualties in earthquake-stricken landslides has not yet been developed. Existing studies on this topic suffer from two main problems: firstly, the prevalence of static population assumptions neglects dynamic population characteristics, leading to systematic discrepancies between casualty risk assessments and spatial distribution predictions and the actual disaster situation; secondly, the spatial characteristics of environmental factors and their complex correlation mechanisms are often simplified to single-factor or deterministic mapping relationships, failing to consider the spatial heterogeneity of environmental factors and the spatiotemporal non-stationarity of the relationship between human activities and actual disaster scenarios. The static assumptions about population distribution and the failure to consider the heterogeneity of environmental influencing factors are key issues affecting prediction accuracy, resulting in a significant gap between casualty assessment results and the actual needs of emergency response and rescue operations. Summary of the Invention

[0006] To address the problem of low accuracy in predicting casualties caused by neglecting population dynamics and environmental heterogeneity in existing technologies, this invention provides a method and system for predicting casualties caused by earthquake landslides that considers both population dynamics and environmental heterogeneity. This invention obtains population dynamics at the time of an earthquake through data fusion, and obtains environmental heterogeneity parameters based on a random forest algorithm model. Finally, it utilizes a constructed probabilistic prediction model for earthquake landslide-induced casualties to achieve accurate predictions, thereby improving the accuracy of casualty prediction.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] The first aspect of this invention proposes a method for predicting casualties caused by earthquake-induced landslides, taking into account population dynamics and environmental heterogeneity, comprising:

[0009] Step 1: Collect all environmental factor data and multi-source population distribution data within the target seismic area according to the preset spatial units; among which, the environmental factor data includes topographic factors, geological factors and basic geographic environmental factors, and the multi-source population distribution data includes population kilometer grid data and mobile location data;

[0010] Step 2: Input the collected environmental factor data into the preset logistic regression model to obtain the earthquake landslide probability, which is helpful for predicting the casualties caused by the landslide.

[0011] Step 3: Process multi-source population distribution data using data fusion methods to obtain the dynamic population distribution during the earthquake period, which facilitates the understanding of population dynamic characteristics and improves the accuracy of casualty prediction.

[0012] Step 4: Based on the random forest algorithm model and SHAP method, the environmental factor data is processed to obtain environmental heterogeneity parameters and environmental factor contribution, which makes it easier to integrate the impact of the environment on human casualties into the prediction model to improve prediction accuracy.

[0013] Step 5: Construct a probability prediction model for casualties caused by earthquake landslides based on environmental heterogeneity parameters and the contribution of environmental factors. Input the environmental heterogeneity parameters, earthquake landslide probability, and dynamic population distribution during the earthquake occurrence period into the probability prediction model for casualties caused by earthquake landslides to obtain the probability distribution of casualties caused by earthquake landslides.

[0014] Furthermore, step two specifically includes:

[0015] The Spearman rank correlation formula is used to obtain the correlation between any two environmental factors in all environmental factor data. The environmental factors are then screened based on the correlation to obtain the screened environmental factors, which are used to remove the influencing factors with weak correlation.

[0016] The selected environmental factors are input into a preset logistic regression model to obtain the probability of earthquake landslides.

[0017] Furthermore, step three specifically includes:

[0018] Two-dimensional discrete Fourier transforms were performed on the population kilometer grid data and the mobile location data respectively to obtain the frequency domain image; among which, the mobile location data includes the spatial location dataset of anonymous user equipment within a preset time window before and after the earthquake, collected by the operator's communication network.

[0019] Amplitude information is obtained from the frequency domain image corresponding to the population kilometer grid data, and phase information is obtained from the frequency domain image corresponding to the mobile positioning data.

[0020] By processing amplitude and phase information using the two-dimensional discrete Fourier inverse transform, the dynamic population distribution during the earthquake period can be obtained, which helps to improve the accuracy of prediction.

[0021] Furthermore, step four specifically includes:

[0022] Environmental element data are input into a pre-defined random forest algorithm model to obtain environmental heterogeneity parameters;

[0023] Based on the random forest algorithm model, the contribution of environmental factors is obtained using the SHAP method and the Gini index method.

[0024] Furthermore, the method of obtaining the contribution of environmental factors based on the SHAP method and the Gini index method on the basis of the random forest algorithm model specifically includes:

[0025] The feature contribution values ​​are calculated based on the environmental heterogeneity parameters, and an n×m feature contribution value matrix is ​​constructed based on the feature contribution values, where n is the sample size and m is the number of features;

[0026] Calculate the decrease in Gini value for each feature when splitting nodes in the random forest algorithm model, and weight and fuse the feature contribution matrix with the Gini decrease to obtain the importance score of the environmental factor.

[0027] The contribution of environmental factors is obtained by normalizing the importance scores of environmental factors.

[0028] Furthermore, the probabilistic prediction model for earthquake-induced landslide casualties constructed based on environmental heterogeneity parameters and the contribution of environmental factors specifically includes:

[0029] The environmental factors are ranked according to their contribution to the overall environmental factors, and the top two environmental factors are obtained. The environmental correction coefficient is then obtained based on the top two environmental factors and the environmental heterogeneity parameter.

[0030] A probability prediction model for casualties caused by earthquake-induced landslides was constructed based on environmental correction coefficients.

[0031] Furthermore, the environmental correction factor is expressed by the following formula:

[0032]

[0033]

[0034]

[0035] in, This is the environmental correction factor. For environmental heterogeneity parameters, and These are different normalized environmental factors. For coefficients, Let y be a composite function, and y be the distribution of casualties in real historical earthquake cases.

[0036] Furthermore, the probability prediction model for casualties caused by earthquake-induced landslides is expressed by the following formula:

[0037]

[0038] in, R pop The probability distribution of casualties in earthquake-induced landslides. H landslide This represents the probability of an earthquake-induced landslide. P dynamic This represents the dynamic population distribution during the period in which the earthquake occurred. Euncertainty This represents the environmental heterogeneity parameter.

[0039] A second aspect of this invention proposes a system for predicting casualties caused by earthquake-induced landslides, taking into account population dynamics and environmental heterogeneity, comprising:

[0040] The data collection module is used to collect all environmental factor data and multi-source population distribution data in the target seismic area according to the preset spatial units; among which, the environmental factor data includes topographic factors, geographical factors and basic geographic environmental factors, and the multi-source population distribution data includes population kilometer grid data and mobile positioning data.

[0041] The landslide prediction module is used to input the collected environmental factor data into a preset logistic regression model to obtain the probability of earthquake landslides, which facilitates the subsequent prediction of casualties caused by landslides.

[0042] The dynamic population distribution module is used to process multi-source population distribution data using data fusion methods to obtain the dynamic population distribution during the earthquake period, which facilitates the acquisition of population dynamic characteristics and improves the accuracy of casualty prediction.

[0043] The element extraction module is used to process environmental factor data based on the random forest algorithm model and SHAP method to obtain environmental heterogeneity parameters and environmental factor contribution, which makes it easier to integrate the impact of the environment on human casualties into the prediction model to improve prediction accuracy.

[0044] The prediction module is used to construct a probability prediction model for casualties caused by earthquakes and landslides based on environmental heterogeneity parameters and the contribution of environmental factors. The environmental heterogeneity parameters, the probability of earthquakes and landslides, and the dynamic population distribution during the earthquake occurrence period are input into the probability prediction model for casualties caused by earthquakes and landslides to obtain the probability distribution of casualties caused by earthquakes and landslides.

[0045] The beneficial effects of this invention are:

[0046] This invention improves the accuracy of casualty assessment caused by earthquakes and landslides, providing a reliable basis for earthquake rescue action planning. Regarding prediction accuracy, it integrates the spatial accuracy of kilometer-grid population data with the dynamic real-time nature of mobile positioning data, overcoming the shortcomings of insufficient spatiotemporal resolution in traditional static population models. In terms of timeliness, it determines device locations using mobile positioning data, which can meet the critical 72-hour emergency response requirement with 5-minute updates to operator signaling data. Regarding environmental heterogeneity parameters, it employs the nonlinear coupling effect of random forest quantification of multidimensional factors, significantly improving the spatial prediction accuracy of landslide burial casualty rates. Attached Figure Description

[0047] Figure 1A flowchart illustrating a method for predicting casualties caused by earthquake-induced landslides, taking into account population dynamics and environmental heterogeneity, is provided for embodiments of the present invention.

[0048] Figure 2 This is a flowchart illustrating the training of a method for predicting casualties caused by earthquake landslides, taking into account population dynamics and environmental heterogeneity, as provided in an embodiment of the present invention.

[0049] Figure 3 This is an architecture diagram of an earthquake landslide casualty prediction system that takes into account population dynamics and environmental heterogeneity, provided as an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0051] Example 1

[0052] like Figure 1 As shown, a method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, includes:

[0053] S101: Collect all environmental factor data and multi-source population distribution data within the target seismic area according to the preset spatial units; among which, the environmental factor data includes topographic factors, geographical factors and basic geographic environmental factors, and the multi-source population distribution data includes population kilometer grid data and mobile positioning data.

[0054] S102: Input the collected environmental factor data into the preset logistic regression model to obtain the earthquake landslide probability.

[0055] S103: Use data fusion methods to process multi-source population distribution data to obtain the dynamic population distribution during the earthquake period.

[0056] S104: Based on the random forest algorithm model and SHAP method, environmental factor data are processed to obtain environmental heterogeneity parameters and environmental factor contribution.

[0057] S105: Construct a probability prediction model for casualties caused by earthquake landslides based on environmental heterogeneity parameters and the contribution of environmental factors. Input environmental heterogeneity parameters, earthquake landslide probability, and dynamic population distribution during the earthquake occurrence period into the probability prediction model for casualties caused by earthquake landslides to obtain the probability distribution of casualties caused by earthquake landslides.

[0058] This invention first obtains the probability of earthquake landslides through environmental factors, then obtains the dynamic distribution of the population using population kilometer grid data and mobile positioning data, which can more accurately determine the distribution of people during an earthquake. Finally, the environmental factors are processed to obtain environmental heterogeneity parameters and the contribution of environmental factors. Based on the environmental heterogeneity parameters, a probability prediction model for earthquake landslide-related casualties is constructed, and the probability distribution of earthquake landslide-related casualties is obtained from the model. This invention combines dynamic population distribution and environmental heterogeneity to improve the accuracy of prediction.

[0059] Example 2

[0060] Based on the above embodiments, such as Figure 2 As shown, this invention proposes a specific process for a method to predict casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, including:

[0061] S201: Collect earthquake case data, including earthquake intensity, earthquake casualty records, co-seismic landslide data, multi-source population distribution data in earthquake zones, and basic geographical, topographical, geological, meteorological and other environmental data within earthquake-affected areas.

[0062] Specifically, data on earthquake cases and their consequences will be collected, including earthquake intensity distribution data and earthquake casualty records. Data on coseismic landslides will be collected, primarily focusing on the spatial distribution of coseismic landslides occurring during earthquake events, including their location and spatial geometry. Environmental factor data will be collected, including at least topographic factors (such as elevation, slope, aspect, surface relief, surface curvature, and topographic humidity index), geological factors (such as stratigraphic lithology and fault distribution), and basic geographical environmental factors (such as soil properties, vegetation characteristics, water system distribution, road distribution, and building distribution). Multi-source population data for the earthquake zone will be collected, including at least kilometer-grid data of population distribution in the earthquake zone and mobile location data during the earthquake's occurrence period. Multi-source population distribution data includes both kilometer-grid population data and mobile location data.

[0063] S202: Using the logistic regression algorithm and the modeling approach of earthquake landslide probability hazard assessment, an earthquake landslide probability hazard assessment model is constructed based on earthquake landslide samples and multi-factor environmental factors to obtain earthquake landslide probability distribution data.

[0064] Specifically, using coseismic landslide data, we selected environmental factors that influence earthquake-triggered landslides, and used the landslide / non-landslide area ratio to select negative samples to create a sample dataset. The training set accounted for 70% to 80% of the sample data, and the validation set accounted for 20% to 30%.

[0065] The Spearman rank correlation test is used to examine the correlation between various environmental factors, quantify the degree of correlation between them, and select environmental factors for modeling. The Spearman rank correlation is calculated as follows:

[0066]

[0067] in, Let i be the i-th element in variable x. Let be the i-th element in variable y, and N be the total sample size. Let y be the average value of the variable. Let x be the average value of the variable.

[0068] Using the selected environmental factors and landslide sample dataset as input variables, a hazard assessment model was established by training the model using the training dataset. The hazard assessment model was a logistic regression model. The trained model was validated using a validation set, and the predicted results were compared with those of geological hazard samples. Validation metrics included accuracy, recall, F1 score, ROC curve, and AUC value. Using the constructed and trained assessment model, the probability of a landslide occurring at any spatial location under seismic intensity was calculated, and earthquake landslide probability distribution data were obtained.

[0069] S203: Based on the data fusion method, extract multi-dimensional population information from the population kilometer grid data of the earthquake zone and the mobile positioning data during the earthquake occurrence period, including high-precision spatial distribution and high-time dynamic features, to obtain dynamic population feature data that can characterize the real-time population distribution during the earthquake occurrence period.

[0070] Specifically, through two-dimensional discrete Fourier transform, the population kilometer grid data and mobile positioning data are transformed into a frequency domain image represented by amplitude spectrum and phase spectrum. The two-dimensional discrete Fourier transform can be expressed as:

[0071]

[0072] in, F(u, v) For frequency domain images, u =0, 1, 2, …, v =0, 1, 2, …, M represents the data in the row direction of the population kilometer grid data, N represents the data in the column direction of the population kilometer grid data, and f(x,y) is the original population kilometer grid data.

[0073] The amplitude information of the population distribution to be merged is obtained by calculating the frequency domain image corresponding to the population kilometer grid data, and the phase information of the population distribution to be merged is obtained by calculating the frequency domain image corresponding to the mobile positioning data.

[0074] Dynamic population distribution characteristics data with high reliability and timeliness are obtained by using the two-dimensional discrete Fourier inverse transform. The inverse Fourier transform can be expressed as:

[0075]

[0076] in, DFT -1 This represents the inverse Fourier transform. F h (u, v) A frequency domain image representing the population distribution to be merged. A h (u, v) and Ψ h (u, v) These represent the amplitude spectrum and phase spectrum of the frequency domain image, respectively. This represents a dynamic population distribution.

[0077] S204: Using the actual distribution of casualties in earthquake landslides and combining various environmental factors that may affect the distribution of casualties, a random forest algorithm is used to construct an environmental heterogeneity evaluation model for casualties in earthquake landslides and obtain environmental heterogeneity parameters.

[0078] Specifically, environmental factors are constructed by selecting data on elevation and its topographic parameters, strata lithology, faults, soil types, land use types, vegetation cover, water systems, roads, buildings, and other environmental factors.

[0079] By using correlation analysis and environmental factor elimination, we can ensure that there is little spatial autocorrelation among the environmental factors.

[0080] For negative sample selection, negative samples will be randomly generated in areas more than 100 meters away from positive samples within the earthquake zone at a ratio of 1:10 to construct a training sample set for the random forest algorithm. Using the training dataset, an environmental heterogeneity assessment model affecting the distribution of casualties will be established to obtain the assessment results characterizing the environmental heterogeneity of casualties in earthquake-induced landslides. The machine learning interpretability SHAP method will be used, employing the Gini index method to give the importance score (VIM) of environmental factors and obtain their contribution.

[0081] Specifically, feature contribution values ​​are calculated based on environmental heterogeneity parameters. An n×m feature contribution value matrix is ​​then constructed, where n is the sample size and m is the number of features. The Gini value decrease for each feature at the split node in the random forest algorithm model is calculated. The feature contribution value matrix and the Gini value decrease are then weighted and fused to obtain the importance score of the environmental factor. The importance score of the environmental factor is then normalized to obtain the contribution degree of the environmental factor.

[0082] S205: Construct a probability prediction model for casualties caused by earthquake landslides based on environmental heterogeneity parameters and the contribution of environmental factors. Input environmental heterogeneity parameters, earthquake landslide probability, and dynamic population distribution during the earthquake occurrence period into the probability prediction model for casualties caused by earthquake landslides to obtain the probability distribution of casualties caused by earthquake landslides.

[0083] Specifically, based on modeling case data and following the assessment approach of "earthquake landslide hazard - dynamic population exposure - environmental factor heterogeneity," a probability prediction model for casualties caused by earthquake landslides is constructed, which can be expressed as:

[0084]

[0085] in, R pop The probability distribution of casualties in earthquake-induced landslides. H landslide This represents the probability of an earthquake-induced landslide. P dynamic This represents the dynamic population distribution during the period in which the earthquake occurred. E uncertainty This represents the environmental heterogeneity parameter.

[0086] Based on the ranking of environmental factors by their contribution, the two dominant environmental factors were identified. x a , x b Based on the environmental heterogeneity assessment results, the environmental correction coefficient for predicting the probability of casualties in earthquake-induced landslides is calculated. k , can be represented as:

[0087]

[0088]

[0089]

[0090] in, The environmental correction coefficient is spatially distributed data, representing the correction coefficient on each grid. For environmental heterogeneity parameters, and These are different normalized environmental factors. cov Describing covariance, The coefficient can be obtained from real historical earthquake cases. Let y be a comprehensive function, representing a linear or nonlinear function that comprehensively considers the environmental impact of multiple factors, where y represents the distribution of casualties in real historical earthquake cases.

[0091] Calculate the number of casualties and the spatial probability distribution caused by earthquake-induced landslides.

[0092] Example 3

[0093] Based on the above embodiments, the present invention proposes a specific process for a method to predict casualties caused by earthquake landslides, considering population dynamics and environmental heterogeneity, including:

[0094] S301: Collect all environmental factor data and multi-source population distribution data within the target seismic area according to the preset spatial units; among which, environmental factor data includes topographic factors, geographical factors and basic geographic environmental factors, and multi-source population distribution data includes population kilometer grid data and mobile positioning data.

[0095] S302: Input the collected environmental factor data into the preset logistic regression model to obtain the earthquake landslide probability.

[0096] Specifically, the Spearman rank correlation formula is used to obtain the correlation between any two environmental factors in all environmental factor data. The environmental factors are then screened based on the correlation to obtain the screened environmental factors.

[0097] The selected environmental factors are input into a preset logistic regression model to obtain the probability of earthquake landslides.

[0098] S303: Use data fusion methods to process multi-source population distribution data to obtain the dynamic population distribution during the earthquake period.

[0099] Specifically, two-dimensional discrete Fourier transforms were performed on the population kilometer grid data and the mobile location data to obtain a frequency domain image; among which, the mobile location data included an anonymous user equipment spatial location dataset collected by the operator's communication network within a preset time window before and after the earthquake.

[0100] Amplitude information is obtained from the frequency domain image corresponding to the population kilometer grid data, and phase information is obtained from the frequency domain image corresponding to the mobile positioning data.

[0101] The amplitude and phase information are processed by the two-dimensional discrete Fourier inverse transform to obtain the dynamic population distribution during the earthquake period.

[0102] S304: Based on the random forest algorithm model and SHAP method, environmental factor data are processed to obtain environmental heterogeneity parameters and environmental factor contributions.

[0103] Specifically, environmental element data is input into a pre-defined random forest algorithm model to obtain environmental heterogeneity parameters. Environmental heterogeneity parameters represent the non-uniform (heterogeneous) distribution characteristics exhibited in the spatial dimension after the combined effects of all environmental elements.

[0104] Environmental factor contributions are derived using the SHAP and Gini index methods based on a random forest algorithm model. Specifically, feature contribution values ​​are calculated based on environmental heterogeneity parameters, and an n×m feature contribution value matrix is ​​constructed, where n is the sample size and m is the number of features. The Gini value decrease for each feature at the split node in the random forest algorithm model is calculated, and the feature contribution value matrix and the Gini value decrease are weighted and fused to obtain the importance score of the environmental factor. The importance score of the environmental factor is then normalized to obtain the environmental factor contribution.

[0105] S305: Construct a probability prediction model for casualties caused by earthquake landslides based on environmental heterogeneity parameters and the contribution of environmental factors. Input environmental heterogeneity parameters, earthquake landslide probability, and dynamic population distribution during the earthquake occurrence period into the probability prediction model for casualties caused by earthquake landslides to obtain the probability distribution of casualties caused by earthquake landslides.

[0106] Specifically, the probability prediction model for casualties caused by earthquake-induced landslides is expressed by the following formula:

[0107]

[0108] in, R pop The probability distribution of casualties in earthquake-induced landslides. H landslide This represents the probability of an earthquake-induced landslide. P dynamic This represents the dynamic population distribution during the period in which the earthquake occurred. E uncertainty This represents the environmental heterogeneity parameter.

[0109] Based on the ranking of environmental factors by their contribution, the two dominant environmental factors were identified. x a , x b Based on the environmental heterogeneity assessment results, the environmental correction coefficient for predicting the probability of casualties in earthquake-induced landslides is calculated. k , can be represented as:

[0110]

[0111]

[0112]

[0113] in, The environmental correction coefficient is spatially distributed data, representing the correction coefficient on each grid. For environmental heterogeneity parameters, and These are different normalized environmental factors. cov Describing covariance, The coefficient can be obtained from real historical earthquake cases. Let y be a comprehensive function, representing a linear or nonlinear function that comprehensively considers the environmental impact of multiple factors, where y represents the distribution of casualties in real historical earthquake cases.

[0114] Example 4

[0115] Based on the above embodiments, such as Figure 3 As shown, this invention proposes a system for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, comprising:

[0116] The data collection module is used to collect all environmental element data and multi-source population distribution data in the target earthquake area according to the preset spatial units; among which, the environmental element data includes earthquake landslide influencing factors, and the multi-source population distribution data includes population kilometer grid data and mobile positioning data;

[0117] The landslide prediction module is used to input the collected environmental factor data into a preset logistic regression model to obtain the probability of earthquake landslides.

[0118] The dynamic population distribution module is used to process multi-source population distribution data using data fusion methods to obtain the dynamic population distribution during the earthquake period.

[0119] The element extraction module is used to process environmental element data based on the random forest algorithm model and SHAP method to obtain environmental heterogeneity parameters and environmental factor contribution.

[0120] The prediction module is used to construct a probability prediction model for casualties caused by earthquakes and landslides based on environmental heterogeneity parameters and the contribution of environmental factors. The environmental heterogeneity parameters, the probability of earthquakes and landslides, and the dynamic population distribution during the earthquake occurrence period are input into the probability prediction model for casualties caused by earthquakes and landslides to obtain the probability distribution of casualties caused by earthquakes and landslides.

[0121] It should be noted that the earthquake landslide casualty prediction system provided in this embodiment of the invention, which considers population dynamics and environmental heterogeneity, is for the purpose of implementing the above-mentioned earthquake landslide casualty prediction method that considers population dynamics and environmental heterogeneity. Its specific functions can be referred to in the above-mentioned method embodiments, and will not be repeated here.

[0122] In summary, this invention improves the accuracy of assessing casualties caused by earthquakes and landslides, providing a reliable basis for developing action plans for earthquake relief. Regarding prediction accuracy, the integration of spatial accuracy from kilometer-grid population data with the dynamic real-time nature of mobile positioning data overcomes the shortcomings of insufficient spatiotemporal resolution in traditional static population models. In terms of timeliness, determining device locations using mobile positioning data, even with 5-minute updates to operator signaling data, meets the critical 72-hour window for emergency response. Regarding environmental heterogeneity parameters, the use of random forest quantification of the nonlinear coupling effect of multidimensional factors significantly improves the spatial prediction accuracy of landslide burial casualty rates.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, characterized in that... include: Step 1: Collect all environmental factor data and multi-source population distribution data within the target earthquake area according to the preset spatial units; among which, the environmental factor data includes topographic factors, geological factors and basic geographic environmental factors, and the multi-source population distribution data includes population kilometer grid data and mobile location data; Step 2: Input the collected environmental factor data into the preset logistic regression model to obtain the earthquake landslide probability; Step 3: Process the multi-source population distribution data using data fusion methods to obtain the dynamic population distribution during the earthquake period; Step 4: Process the environmental factor data based on the random forest algorithm model and the SHAP method to obtain environmental heterogeneity parameters and environmental factor contributions; Step 5: Construct a probability prediction model for casualties caused by earthquake landslides based on environmental heterogeneity parameters and the contribution of environmental factors. Input the environmental heterogeneity parameters, earthquake landslide probability, and dynamic population distribution during the earthquake occurrence period into the probability prediction model for casualties caused by earthquake landslides to obtain the probability distribution of casualties caused by earthquake landslides.

2. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 1, is characterized in that... Step two specifically includes: The correlation between any two environmental factors in all environmental factor data is obtained by using the Spearman rank correlation formula. The environmental factors are then filtered based on the correlation to obtain the filtered environmental factors. The selected environmental factors are input into a preset logistic regression model to obtain the probability of earthquake landslides.

3. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 1, is characterized in that... Step three specifically includes: Two-dimensional discrete Fourier transforms were performed on the population kilometer grid data and the mobile location data respectively to obtain the frequency domain image; among which, the mobile location data includes the spatial location dataset of anonymous user equipment within a preset time window before and after the earthquake, collected by the operator's communication network. Amplitude information is obtained from the frequency domain image corresponding to the population kilometer grid data, and phase information is obtained from the frequency domain image corresponding to the mobile positioning data. The amplitude and phase information are processed by the two-dimensional discrete Fourier inverse transform to obtain the dynamic population distribution during the earthquake period.

4. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 1, is characterized in that... Step four specifically includes: Environmental factor data are input into a pre-defined random forest algorithm model to obtain environmental heterogeneity parameters; Based on the random forest algorithm model, the contribution of environmental factors is obtained using the SHAP method and the Gini index method.

5. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 4, is characterized in that... The specific steps for obtaining the environmental factor contribution based on the SHAP method and Gini index method on the basis of the random forest algorithm model include: The feature contribution values ​​are calculated based on the environmental heterogeneity parameters, and an n×m feature contribution value matrix is ​​constructed based on the feature contribution values, where n is the sample size and m is the number of features; Calculate the Gini value decrease for each feature when splitting nodes in the random forest algorithm model, and weight and fuse the feature contribution matrix with the Gini value decrease to obtain the importance score of the environmental factor. The contribution of environmental factors is obtained by normalizing the importance scores of environmental factors.

6. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 4, is characterized in that... The probabilistic prediction model for earthquake-induced landslide casualties, constructed based on environmental heterogeneity parameters and the contribution of environmental factors, specifically includes: The environmental factors are ranked according to their contribution to the overall environmental factors, and the top two environmental factors are obtained. The environmental correction coefficient is then obtained based on the top two environmental factors and the environmental heterogeneity parameter. A probability prediction model for casualties caused by earthquake-induced landslides was constructed based on environmental correction coefficients.

7. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 6, is characterized in that... The environmental correction factor is expressed by the following formula: in, This is the environmental correction factor. For environmental heterogeneity parameters, and These are different normalized environmental factors. For coefficients, Let y be a composite function, where y represents the distribution of casualties in real historical earthquake cases.

8. The method for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, as described in claim 7, is characterized in that... The probability prediction model for casualties caused by earthquake-induced landslides is expressed by the following formula: Among them, R pop H represents the probability distribution of casualties in earthquakes and landslides. landslide Let P be the probability of an earthquake-induced landslide. dynamic E represents the dynamic population distribution during the period in which the earthquake occurred. uncertainty This is a parameter representing environmental heterogeneity.

9. A system for predicting casualties caused by earthquake-induced landslides, considering population dynamics and environmental heterogeneity, characterized in that... include: The data collection module is used to collect all environmental factor data and multi-source population distribution data in the target seismic area according to the preset spatial units; among which, the environmental factor data includes topographic factors, geological factors and basic geographic environmental factors, and the multi-source population distribution data includes population kilometer grid data and mobile positioning data; The landslide prediction module is used to input the collected environmental factor data into a preset logistic regression model to obtain the probability of earthquake landslides. The dynamic population distribution module is used to process multi-source population distribution data using data fusion methods to obtain the dynamic population distribution during the earthquake period. The element extraction module is used to process environmental factor data based on the random forest algorithm model and the SHAP method to obtain environmental heterogeneity parameters and environmental factor contribution. The prediction module is used to construct a probability prediction model for casualties caused by earthquakes and landslides based on environmental heterogeneity parameters and the contribution of environmental factors. The environmental heterogeneity parameters, the probability of earthquakes and landslides, and the dynamic population distribution during the earthquake occurrence period are input into the probability prediction model for casualties caused by earthquakes and landslides to obtain the probability distribution of casualties caused by earthquakes and landslides.

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