196 results about "Landslide susceptibility" patented technology

The invention provides a landslide space prediction method based on a knowledge graph and representation learning. The landslide space prediction method specifically comprises the following steps: S1, constructing a landslide susceptibility knowledge graph considering mechanism knowledge and spatial-temporal characteristics; a knowledge graph ontology mode is designed, landslide disaster field knowledge modeling considering spatial-temporal characteristics and mechanism knowledge is completed, and on this basis, entities are extracted from multi-source data, relationships are constructed, and a knowledge graph data layer is constructed; and S2, through map semantic feature learning based on a ComplEx model, directed weighted graph construction based on a landslide susceptibility knowledge map, equivalent positive and negative landslide sampling based on a Louvain algorithm and map structural feature learning based on a GraphSAGE model, realizing landslide space prediction based on the knowledge map and representation learning. According to the method, diversified feature inductive learning under the constraint of fewer landslide samples in a complex heterogeneous environment can be realized, and the landslide space distribution probability is predicted through a node classification task.

The invention discloses a landslide susceptibility evaluation method and system based on multi-source data and deep learning. The landslide susceptibility evaluation method combines an SBAS-InSAR technology, remote sensing image analysis, a deep learning algorithm, a TRIIGRS-SCOOPS3D coupling model and MASSFLOW simulation. The method is used for high-precision landslide prone area identification, potential landslide position prediction and three-dimensional landslide process simulation, and is especially suitable for landslide disaster risk management and emergency response in mountainous areas.

The invention discloses a landslide susceptibility assessment method and system fusing feature interaction and dynamic weighting, and belongs to the field of geological disaster assessment, and the method comprises the steps: building an evaluation index system, and carrying out the factor classification; acquiring landslide and non-landslide point sample data, evaluating factor importance by adopting a random forest to obtain a preliminary weight, and preliminarily evaluating the susceptibility based on an I-RF model; detecting double-factor interaction by using a geographic detector, screening interaction factors for collaborative enhancement, and optimizing an expansion index system; retraining the random forest model, obtaining a new weight containing a single factor and an interaction factor, and constructing an I-RF optimization model; for spatial heterogeneity, sub-regions are divided by using spatial clustering, and the weight of each sub-region is dynamically corrected according to the sensitivity of a main control factor, so that self-adaptive adjustment is realized; and calculating susceptibility indexes of the sub-regions and the whole region, and grading to form spatialized and multi-level risk partitions. According to the method, the scientificity and accuracy of evaluation are effectively improved through feature interaction optimization and partition dynamic weighting.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention discloses a mine high and steep slope landslide machine learning prediction method based on an unmanned aerial vehicle technology and multi-source data fusion. The method comprises the steps that a high-resolution digital surface model is shot by an unmanned aerial vehicle; removing non-topographic features from the digital surface model data in ArcGIS software, then converting the digital surface model data into digital elevation model data, obtaining a surface refining model of the surface mine in the digital elevation model, taking the surface refining model as a constraint to trim the surface of the three-dimensional geologic model, and fusing factors of ArcGIS software and three-dimensional geologic model analysis by using Python to obtain a three-dimensional geologic model surface refining model; complete slope data are constructed; inputting the fused complete side slope data into a machine learning algorithm training model, and generating a side slope landslide susceptibility prediction model; the optimized model is used for carrying out landslide susceptibility prediction on new slope data. According to the method, various factors influencing the slope stability can be comprehensively analyzed, high efficiency can be kept during large-scale data processing, and the slope stability in mine engineering can be evaluated more accurately.

The invention belongs to the field of computer application technology and geological disaster prediction and early warning, and particularly discloses a landslide real-time prediction method and early warning method based on multi-modal information fusion, and the prediction method comprises the steps: constructing a landslide event text data set; a BERT-BiLSTM-CRF model is trained after time-space element labeling, and a structured historical landslide knowledge graph is constructed; using the landslide boundary training data set to train a U-Net + + image segmentation model to identify a landslide space boundary; taking time and space information as space-time anchor points, combining with a U-Net + + image segmentation model identification result, and identifying a landslide occurrence range and time on a remote sensing cloud platform; and extracting a multi-dimensional dynamic environment factor by combining a landslide occurrence range and time, constructing a time sequence feature sample set, and training a landslide susceptibility prediction model to realize dynamic prediction of landslide risks. According to the invention, timeliness and space precision are taken into consideration, and prediction accuracy and response capability of landslide disasters can be effectively improved.

The invention discloses a landslide susceptibility assessment method and device considering a factor change trend, and the method comprises the steps: carrying out the cutting processing of time sequence landslide impact factor data in a to-be-assessed target region based on the range of the to-be-assessed target region, obtaining a time sequence landslide influence factor set with consistent range size and corresponding spatial position; quantifying each factor in the factor set into a trend landslide influence factor; performing dimension removal processing on each trend landslide influence factor in the trend landslide influence factor set; performing multi-collinearity test on the trend landslide influence factor set after the dimension removal processing; and inputting the trend landslide influence factor set after the multi-collinearity test into a pre-established landslide susceptibility evaluation model to obtain a landslide susceptibility evaluation result of the to-be-evaluated target area. According to the method, the temporal and spatial variation trend and the cumulative effect of the landslide influence factor can be fully reflected through the quantified trend landslide influence factor, and the precision of landslide susceptibility evaluation is improved.

The invention provides a landslide disaster data processing method, device and equipment, belongs to the technical field of computer information processing, and solves the problems that an existing landslide disaster data processing method is inaccurate and the troubleshooting accuracy of a landslide hidden danger area is not high. The method comprises the following steps: acquiring remote sensing geological data of a target area within a preset target time range; obtaining grid region data according to the remote sensing geological data; obtaining evaluation factor data according to the remote sensing geological data and the grid region data; the evaluation factor data are input into a landslide prediction model for processing, grid area landslide susceptibility data are obtained, the landslide prediction model processes the evaluation factor data through a target function, and the grid area landslide susceptibility data are obtained; and obtaining landslide disaster susceptibility prediction data of the target area according to the landslide susceptibility data of the grid area. According to the scheme, the accuracy and reliability of landslide disaster prediction are improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a landslide susceptibility prediction method based on knowledge map and spatial-temporal feature fusion, and the method comprises the steps: extracting an inference feature vector from a geological knowledge map through a map neural network; extracting a spatial feature vector from the multi-source spatial data by using a convolutional neural network; extracting a time sequence feature vector from the rainfall time sequence data by using a Transform model; generating an attention weight based on the reasoning feature vector, and performing adaptive weighted fusion on the space and time sequence feature vectors by using the weight to obtain a fused feature vector; and inputting the fusion feature vector into the prediction model, and outputting the landslide occurrence probability. The geological priori knowledge in the knowledge graph is introduced to guide the fusion process of the spatial-temporal characteristics, so that the model can focus on the key disaster-inducing factor combination, the accuracy and reliability of prediction are remarkably improved, and the interpretability of the model is enhanced at the same time.

The invention provides a reservoir area landslide susceptibility evaluation method considering reservoir water level change factors. The method comprises the following steps: preparing data; constructing a model; introducing an adjustment coefficient; probability calculation and loss function definition; optimizing parameters; carrying out regularization processing; and outputting a result. The invention mainly considers the influence of reservoir water level change on reservoir area landslide susceptibility evaluation, provides a reservoir area landslide dynamic susceptibility evaluation method considering reservoir water level change, and mainly solves the following problems: a traditional landslide susceptibility evaluation technology is mainly oriented to administrative division, and the method mainly aims at evaluating the reservoir area landslide susceptibility, so that the reservoir area landslide dynamic susceptibility evaluation method can be used for evaluating the reservoir area landslide susceptibility. A river distance is introduced as an important influence factor of reservoir area landslide susceptibility evaluation.

The invention provides a landslide susceptibility evaluation method based on machine learning. The landslide susceptibility evaluation method comprises the following steps: determining an evaluation index and a grading standard; constructing to obtain a landslide susceptibility evaluation model; the landslide susceptibility evaluation model comprises a random forest model, a model hyper-parameter optimization module and a linear weighted combination model; and training the landslide susceptibility evaluation model by using the training sample set to obtain the trained landslide susceptibility evaluation model, and predicting the landslide susceptibility level of each evaluation unit in the research area. The invention provides a landslide susceptibility evaluation method based on machine learning, and the method comprises the steps: accurately obtaining the weight of each evaluation factor through a random forest model, carrying out the linear weighting of each evaluation factor in an evaluation unit, and enabling an obtained comprehensive information amount value to reflect the landslide susceptibility grade of the evaluation unit more accurately, the accuracy of a landslide susceptibility result is improved, and a reliable basis is provided for landslide disaster risk assessment and management.

The invention relates to the field of geological disaster prediction, and discloses a landslide susceptibility prediction method in a future scenario, and the method comprises the steps: selecting a landslide influence factor according to a disaster-pregnant environment and disaster-causing factors of a landslide in a research area; after the landslide influence factors are preprocessed, a data set is constructed; constructing a landslide prediction model, and training and verifying the landslide prediction model by using the data set to obtain a model with optimal parameters; replacing an LULC factor and a rainfall factor in the landslide influence factors with an LULC factor simulated by a PLUS model and a rainfall factor simulated by an atmospheric circulation model respectively; and inputting other factors in the simulated LULC factor, the simulated rainfall factor and the landslide influence factor into the model with the optimal parameters to obtain a future scene landslide susceptibility prediction result. The method has the advantages that the defect that landslide susceptibility prediction is conducted only by simulating a single landslide influence factor is overcome, and the prediction result is more reliable.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses a landslide susceptibility evaluation method based on a time sequence InSAR, and the method comprises the steps: obtaining a surface deformation rate through the inversion of an SAR image time sequence of a target monitoring region, and rendering the surface deformation rate into an RGB color image; inputting the color image into a trained landslide hidden danger identification model to identify a landslide hidden danger area; if the landslide hidden danger area is detected, continuously carrying out deformation monitoring on the area; for each grid of the target monitoring area, calculating the total information amount formed by the multi-source influence factors; wherein the multi-source influence factor comprises a deformation rate and a plurality of static influence factors; and inputting the total information amount of the multi-source influence factors of each grid of the target monitoring area into a landslide susceptibility evaluation model to obtain a susceptibility grade corresponding to each grid of the target monitoring area. According to the method, the identification efficiency of the landslide hidden danger and the accuracy of landslide susceptibility evaluation can be remarkably improved, and the method is suitable for monitoring and evaluation of geological disasters such as mountain landslide, urban settlement and mining area surface deformation.

The embodiment of the invention provides a landslide susceptibility prediction method, system and device and a computer readable storage medium. The method comprises the steps of obtaining a to-be-divided topographic image; wherein the topographic image is a slope direction and mountain shadow map extracted based on the digital topographic image; performing region division on the topographic image through a multi-scale algorithm to obtain slope units; wherein the multi-scale algorithm is used for carrying out slope unit division on the terrain; based on the slope unit, environment factor extraction is carried out, and multiple evaluation factors are determined; performing landslide susceptibility prediction on the slope unit based on the plurality of evaluation factors through a landslide susceptibility prediction model to obtain a landslide susceptibility prediction result corresponding to the slope unit; wherein the landslide susceptibility prediction model comprises a self-organizing mapping neural network module, an information amount module and a support vector machine module; the landslide susceptibility prediction model is a model which is trained by adopting historical landslide data and is used for carrying out landslide prediction on the slope unit. According to the scheme, the accuracy of landslide susceptibility prediction can be improved.

The invention discloses a multi-path library bank landslide susceptibility evaluation method and device based on dynamic-static feature fusion and a storage medium. The method comprises the steps of obtaining multi-source monitoring data including dynamic InSAR deformation time sequence data and static environment influence factors in a target area and a landslide catalog library; decomposing the dynamic InSAR deformation time series data into a dynamic trend term and a periodic term by adopting an empirical mode decomposition method; the multi-source monitoring data and the landslide catalog library are preprocessed, and data sets are divided; constructing a multi-path dynamic-static feature coupling model, and training and outputting a reservoir bank landslide susceptibility evaluation model based on dynamic-static feature coupling; inputting the multi-source monitoring data of the to-be-detected area into the trained landslide susceptibility evaluation model to obtain a susceptibility evaluation index, and determining a prediction result according to the evaluation index; according to the method, the problems of high static data dependence, dynamic deformation feature deficiency, insufficient real-time early warning capability and the like of a traditional landslide susceptibility zoning method are solved.

The invention provides a large-range landslide susceptibility evaluation method and system based on a physical constraint U-Net model, and relates to the technical field of landslide disasters, and the method comprises the steps: obtaining an evaluation factor of a target region, and constructing a historical data set related to the evaluation factor and a landslide region; combining a U-Net model and physical constraints to construct a landslide susceptibility evaluation model; training the landslide susceptibility evaluation model through the historical data set; and inputting the real-time evaluation factor of the target area into the trained landslide susceptibility evaluation model for prediction to obtain a large-range landslide susceptibility evaluation result of the target area. The method solves the problems that the evaluation result obtained by the existing landslide susceptibility evaluation method has lower spatial resolution compared with the original input data, and the evaluation result is poor in effect when the evaluation area is larger.

The invention relates to a landslide susceptibility evaluation method considering InSAR and a multi-stage optimization random forest model. According to the method, non-landslide points are selected through multi-factor control and spatial stratified sampling, feature factors are selected by using a random forest classifier in combination with a Pearson's correlation coefficient matrix and a VIF method, a random forest model is optimized based on a Bayesian algorithm, finally, the performance of the model is evaluated through multiple indexes, and susceptibility indexes are divided into five grades. Compared with the prior art, the method not only takes the earth surface deformation rate identified by InSAR as a characteristic factor, but also performs quantitative analysis on the characteristic factor and the susceptibility zoning result, verifies the consistency of the two factors, can effectively improve the precision of a landslide susceptibility evaluation model, provides important reference for landslide disaster prevention and reduction research, and has a wide application prospect. The method is especially suitable for areas with frequent geological disasters such as southwest regions in China.

The invention provides a landslide susceptibility evaluation method, device, equipment and medium based on InSAR, and the method comprises the steps: obtaining landslide basic data, extracting evaluation index data, analyzing the multicollinearity among the evaluation index data, and constructing a landslide susceptibility evaluation index system; selecting all vector points in a disaster body range of the landslide record library as landslide positive samples, selecting non-landslide negative samples, and constructing training set and test set samples according to the landslide positive samples and the non-landslide negative samples; based on the random forest model and the RF-InSAR model of the sample after the SBAS-InSAR optimization, constructing a landslide susceptibility evaluation model; and training a landslide susceptibility evaluation model by using the training set and the test set samples, and inputting the to-be-analyzed data into the landslide susceptibility evaluation model to evaluate the landslide susceptibility, so as to solve the problem of accurately and reliably evaluating the landslide susceptibility under the condition of comprehensively considering various influence factors.

The invention discloses a landslide sensitivity assessment method and device based on static and dynamic data, and the method comprises the steps: firstly obtaining the current static data of a target region after preprocessing, and inputting the current static data into a trained scoring model; obtaining a static landslide sensitivity score of the target area and dividing a sensitive area of the target area according to the score; obtaining current dynamic data of the high-risk area in the target area after preprocessing, and inputting the current dynamic data into the trained time sequence prediction model to obtain a risk index of the high-risk area in the target area; and finally, comprehensively evaluating the landslide sensitivity of the target area based on the sensitivity subarea of the target area and the risk index of the medium-high risk area. According to the method, the static landslide sensitivity scoring is performed on the static data of the target area, and the trend analysis of the dynamic data of the high-risk area in the target area is combined, so that the potential sliding risk of the landslide of the target area under multiple influence conditions can be comprehensively captured, and the evaluation accuracy is improved.

The invention relates to the technical field of geological disaster assessment, in particular to a non-landslide sample selection optimization method and system based on information amount, and the method comprises the steps: collecting historical landslide data and environmental factor data in a to-be-predicted region, carrying out the importance evaluation of environmental factors, and screening out landslide characteristic factors which have large impact on the landslide susceptibility; carrying out information quantity analysis on the screened landslide characteristic factors, carrying out weight assignment on the factor information quantity according to the importance of the landslide characteristic factors to obtain comprehensive information quantity, and selecting reliable non-landslide samples in an area with relatively low comprehensive information quantity; inputting the data into a prediction model to obtain a prediction result; and carrying out landslide susceptibility evaluation on the to-be-predicted region, generating a landslide susceptibility partition map, and formulating targeted disaster prevention and reduction measures. According to the method, a machine learning algorithm and an information amount method are combined, the objectivity of weight assignment of the influence factors is improved, so that more representative non-landslide samples are selected, and the precision of a landslide susceptibility prediction model is improved.

The invention belongs to the technical field of geological disaster prediction and prevention, and relates to a watershed landslide susceptibility evaluation method based on a weighted information amount model, which comprises the following steps: acquiring landslide point data and multi-source environmental factor data of a target area, constructing a multi-source driving factor database, and carrying out preprocessing; calculating an interpretation force value of each factor by using a geographic detector, and generating a dynamic weight coefficient through linear stretching; calculating an information amount value of each factor category by using a traditional information amount model, and combining the information amount value with the obtained dynamic weight coefficient to generate a weighted information amount value; according to the method, natural and humanistic factors are fused, and a structure breaking-extreme rainfall-engineering disturbance collaborative disaster-causing mechanism is disclosed; driving mechanism differences are analyzed according to drainage basin sections, and the applicability of large-scale region evaluation is improved; compared with a traditional IVM, the AUC value is increased from 0.795 to 0.875, and the misjudgment rate of an extremely high risk area is reduced by 30%.

The invention relates to a landslide susceptibility evaluation method and device, electronic equipment and a medium, and belongs to the technical field of geological disaster prediction and prevention, and the method comprises the steps: obtaining preprocessed digital elevation model data and multi-source data of a target area; dividing the preprocessed digital elevation model data based on preset geographic software to obtain evaluation unit data, the evaluation unit data including slope unit data and grid unit data; training a preset deep integration model based on the evaluation unit data, and determining the evaluation unit data with high training precision as target evaluation unit data; determining a landslide influence factor based on the target evaluation unit data and the multi-source data; inputting the landslide influence factor into a preset depth integration model to obtain a preliminary evaluation result of the landslide susceptibility of the target area; and obtaining a landslide susceptibility evaluation result of the target area based on the preliminary evaluation result. According to the invention, landslide disaster risk zoning and prevention and control schemes can be accurately recommended.

The invention relates to a method for predicting landslide susceptibility by using a machine learning model and SBAS-InSAR deformation, and the method comprises the steps: obtaining an environment static factor and landslide catalog in a sample region, obtaining a dynamic factor based on an SBAS-InSAR technology, building a landslide prediction model based on a random forest algorithm, and carrying out the prediction of the landslide susceptibility. Inputting the static factor, the dynamic factor and the landslide record into a landslide prediction model for training; and inputting the environmental static factor and the dynamic factor of the to-be-detected area into the trained landslide prediction model to obtain a landslide susceptibility map. According to the landslide susceptibility mapping method, the earth surface deformation rate serves as a dynamic factor to be fused into landslide susceptibility evaluation, a scientific basis is provided for landslide prevention and disaster reduction, the earth surface rail rising deformation rate and the earth surface rail falling deformation rate are obtained, the earth surface deformation rate is integrated into a static factor data set, and the landslide susceptibility mapping accuracy is improved. After uniform data preprocessing is completed, static factor and dynamic factor data are input into the landslide prediction model for joint training and evaluation, and the method has the advantage of being high in precision.

The invention relates to the technical field of geological disaster risk analysis, in particular to a landslide susceptibility evaluation method based on federated learning and UNet, and the method comprises the following steps: S1, obtaining landslide influence factor data: collecting a plurality of landslide influence factor data; s2, establishing a federated learning framework: establishing the federated learning framework; s3, designing a UNet deep learning model: feature extraction and segmentation; s4, federal learning model training and parameter integration: updating a global UNet-based deep learning model; s5, performing federated learning iteration and model optimization: optimizing UNet-based deep learning model parameters; s6, generating a landslide susceptibility characteristic graph and probability regression: outputting a landslide susceptibility evaluation result; s7, result verification and disaster prevention and reduction suggestions: providing landslide risk early warning and disaster prevention and reduction suggestions; according to the invention, more efficient and more accurate landslide susceptibility evaluation can be realized, and the model can be updated in time to cope with new data changes.

The invention discloses a landslide susceptibility evaluation method fusing spatial neighborhood information and a random forest, and belongs to the field of landslide susceptibility evaluation.The method comprises the steps that a historical landslide standardized data set is obtained; marking unit attributes of the slope units according to the historical landslide standardized data set; constructing an adjacent matrix, and calculating spatial lag term features according to the adjacent matrix; splicing the space lag item features with the original features to obtain extended features of each slope unit; training a random forest model by using the extension features of each slope unit to obtain a trained random forest model; and mapping the landslide probability value predicted by the random forest model to each slope unit, generating a continuous susceptibility probability distribution map, and marking the susceptibility risk level of each slope unit. According to the method, the problems of insufficient utilization of spatial neighborhood information and low model precision caused by the fact that only attribute information of spatial data is considered by using a traditional random forest model in an existing method are solved.

The invention discloses a co-seismic landslide susceptibility evaluation method and system based on heterogeneous ensemble learning, relates to the field of co-seismic landslide susceptibility evaluation, and solves the problem in the prior art that a deep learning model is difficult to capture the internal relationship between landslide occurrence and disaster-inducing factors through single-machine learning under complex and hard mountain conditions. According to the method, landslide boundary vector data and disaster-inducing factor raster data are integrated, and invalid disaster-inducing factors are removed according to correlation analysis and importance analysis to obtain a training data set; constructing a CNN-RXStack landslide susceptibility evaluation model to predict co-seismic landslide susceptibility of an earthquake area, converting the predicted landslide occurrence probability into a landslide susceptibility grade to obtain a landslide susceptibility map, and verifying and analyzing a landslide susceptibility result; according to the method, the heterogeneity of the disaster-inducing factor characteristics is improved, and the model can better capture the characteristic difference between the landslide and the non-landslide, so that the accuracy and the reliability of model prediction are improved.

The invention relates to a landslide meteorological early warning method coupling machine learning and a rainfall early warning index, which comprises the following steps: taking a frequency ratio of screened landslide evaluation factors in different grading intervals as input, taking a landslide occurrence state as output, adopting a machine learning model to carry out landslide susceptibility modeling, and establishing a landslide susceptibility partition map; modeling a quantitative relation between the rainfall scene and the rainfall early warning index; calculating early effective rainfall and induced event rainfall in different return periods by adopting Gumbel distribution based on historical rainfall statistical data of the to-be-researched area, determining a minimum rainfall early warning index and a maximum rainfall early warning index, and obtaining a rainfall early warning index distribution diagram of the to-be-researched area; and carrying out coupling superposition on the landslide susceptibility partition map and the rainfall early warning index distribution map to obtain a landslide risk meteorological early warning map of the to-be-researched area. According to the method, a scientific basis is provided for regional scale landslide early warning, and the problem of splitting of a traditional model in data utilization and mechanism interpretation is solved.

The invention relates to the field of vulnerability evaluation of extreme rainfall induced mass landslide, in particular to an extreme rainfall mass landslide vulnerability evaluation method fusing diffusion generation and interpretable learning, which comprises the following steps: firstly, constructing a multi-dimensional factor data set, and realizing rasterization preprocessing; secondly, establishing a feature response analysis module, identifying main control factors of landslide occurrence and eliminating redundant variables; then, a denoising diffusion probability model is introduced, and an enhanced balance sample library is constructed; then, carrying out landslide probability prediction based on a multi-algorithm fusion integrated discrimination model, and outputting a grid-level landslide occurrence probability; and finally, minimizing intra-class variance and maximizing inter-class variance by using a natural breakpoint method, determining an optimal grading threshold set, and realizing landslide susceptibility grade division and spatial mapping output. Under the extreme rainfall condition, the method can effectively characterize the susceptibility zoning influence of the mass-occurring landslide flow slip catastrophe process, and achieves high-precision landslide susceptibility evaluation.

The invention discloses a time-space multi-scale feature fusion deep learning landslide susceptibility evaluation method based on number object dual drive, and the method comprises the steps: constructing a time sequence branch + Swindow-Transformer (upper branch) + CNN (lower branch) time-space fusion DL-LSA model, combining an AFF multi-scale space fusion module and a TSF time-space fusion module, and carrying out the deep learning of landslide susceptibility. A complete technical scheme of high-quality sample screening, deep spatial-temporal feature extraction, multi-scale adaptive fusion and dynamic probability prediction is formed, and normal form upgrading of geological disaster risk management from single-drive vector object double-drive and from static evaluation to dynamic early warning is promoted. Comprising the following steps: 1, acquiring influence factors (X); 2, constructing an initial sample set (X-Y pairs); 3, preprocessing the influence factor (X); and 4, influence factor coding (generating a standardized feature X '). And 5, providing physical constraints (optimizing a sample set X '-Y) based on the P-LSA model. And 6, constructing a space-time fusion DL-LSA model. And 7, designing a feature fusion module. And 8, outputting a landslide susceptibility evaluation result.