A dynamic assessment method for mountainous land space defense resilience against geological hazards

Abstract

The present invention discloses a dynamic assessment method for the defensive resilience of mountainous land space facing geological disasters, and relates to the technical field of land space planning. Compared with previous resilience assessment methods, the present invention solves the problem that the traditional resilience assessment method based on subjective qualitative analysis has low assessment efficiency, is greatly affected by individual subjective opinions, and is mostly static assessment, thus affecting the accuracy and reliability of the assessment results; by dividing the evolution of slope geological disasters and their interaction with land space into different stages, respectively using appropriate evaluation methods and models such as machine learning, dynamically quantifying and analyzing the defensive resilience level of mountainous urban systems in the face of different stages of slope geological disasters, and performing optimization analysis to obtain the full-cycle comprehensive resilience of the study area in the face of slope geological disasters, and then performing obstacle analysis to clarify the weak links in the defensive resilience of mountainous urban land space, providing a reference basis for mountainous land space disaster prevention and planning, and having extremely strong practicality.

Description

Technical Field

[0001] The present invention relates to the technical field of land space planning, and in particular to a dynamic assessment method for mountain land space defense resilience against geological disasters. Background Art

[0002] In recent years, with the large-scale development of resources, geological disasters have continued to occur, which not only seriously threatens the safety of life and property of local people, but also seriously endangers the sustainable development of society. The main types of geological disasters are: collapse, landslide, debris flow, ground subsidence, ground fissures, etc. The slope geological disasters (collapse, landslide, debris flow) in mountainous urban areas occur frequently and on a large scale, which greatly restricts the development of social economy. Due to the uncertainty and suddenness of slope geological disasters, passive disaster prevention measures cannot meet the future development needs of mountainous towns. Urban resilience, as a new idea for urban risk governance, plays an important role in achieving the purpose of preventing and mitigating the impact of disasters, protecting people's lives and property, and improving people's sense of living happiness. In addition, there is a relationship of mutual connection and mutual restriction between geological disasters and mountainous urban land space, land use, resource development, etc. Geological disasters restrict the development and utilization of urban space and the development of human economy. The development and utilization of land space, in turn, also affects the occurrence of geological disasters. Unreasonable land space and land development and utilization may trigger and aggravate geological disasters. Improving scientific land space planning can provide source control for geological disaster prevention and control. In the context of the construction of the national land space planning system, "resilient cities" are the main means for mountain towns to cope with disasters in the future. Their construction can effectively reduce the scale of losses in mountain towns under complex disaster scenarios. Their planning concept has been widely recognized and needs to be fully integrated into national land space planning. In the process of compiling national land space planning in my country, resilient planning thinking should be introduced into national land space planning. Therefore, studying a dynamic assessment method for the resilience of mountain town national land space defense for the entire cycle of slope geological disasters will provide a new perspective for disaster prevention and mitigation.

[0003] At present, the methods for evaluating urban disaster resilience mainly include empirical evaluation systems and semi-empirical and semi-quantitative evaluation systems. The evaluation of urban resilience in empirical evaluation systems mainly relies on interviews and evaluations of certain details of the city by experts or teams, such as Uscore2 and the Asian Climate Change Resilience Network (ACCCRN). Uscore2 asks city managers whether they understand the disasters that the city may face or has encountered, whether they have established corresponding disaster prevention plans, etc. The semi-empirical and semi-quantitative evaluation system mainly scores and combines the relevant indicators of the city according to certain weights to obtain the city's overall resilience evaluation in various aspects, such as the Urban Disaster Resilience Score Card (DRSC), the Community Benchmark Resilience Index (BRIC), the City Resilience Index (CPI), etc. Typical representatives include the CPI indicator system, which includes 4 dimensions, 12 goals and 52 indicators. Through quantitative statistical data or answers to questions, the 52 indicators are scored from 1 to 5 points, and then the city's various dimensions and the final urban resilience score are obtained.

[0004] Traditional resilience assessment methods rarely consider the interaction between land space development and utilization and geological hazards. It is difficult to evaluate from the perspective of the full cycle of slope geological hazards and the differentiated development and utilization of mountain urban space. Moreover, they are mostly based on subjective qualitative evaluation, which is inefficient. In addition, although some studies have conducted spatial evaluation and analysis of urban resilience, they only consider the static evaluation of resilience, ignoring the evolution of urban land space resilience in time and space caused by factors such as changes in climate conditions and rapid urban development, and cannot effectively support mountain urban land space planning.

[0005] In order to solve the above problems, the present invention proposes a dynamic assessment method for mountain land space defense resilience oriented to geological disasters. Summary of the invention

[0006] The purpose of the present invention is to propose a dynamic assessment method for mountain land space defense resilience oriented to geological disasters to solve the problems raised in the background technology:

[0007] Traditional resilience assessment methods based on subjective qualitative analysis have low assessment efficiency, are greatly influenced by individual subjective opinions, and are mostly static assessments, which affects the accuracy and reliability of the assessment results.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A dynamic assessment method for mountain land space defense resilience against geological disasters includes the following steps:

[0010] S1: Obtain historical slope geological disaster data and mountain town land space resilience assessment data in the study area, and establish a slope geological disaster defense resilience database in the study area;

[0011] S2: Use machine learning algorithms to build a slope geological disaster resilience assessment model for the pre-disaster prevention stage;

[0012] S3: A combined weighting method combining subjective and objective factors is used to construct a slope geological disaster resilience assessment model for emergency response and post-disaster recovery stages;

[0013] S4: Use multi-attribute decision-making comprehensive evaluation method to evaluate the comprehensive resilience of the study area;

[0014] S5: Based on the evaluation and analysis results of S2-S4, a barrier model is constructed to diagnose the barrier degree of resilience, and a resilience improvement strategy is proposed based on the diagnosis results.

[0015] Preferably, in S2, the slope geological disaster defense resilience assessment model in the pre-disaster prevention stage is optimized and trained with the data obtained in S1, and then the land space resilience assessment of mountain towns in the pre-disaster stage is carried out; by inputting the dynamic resilience data of the pre-disaster prevention stage of different years, the dynamic assessment results of the land space defense resilience in the pre-disaster prevention stage are obtained.

[0016] Preferably, in the process of assessing the spatial resilience of mountainous urban areas in the pre-disaster stage in S2, based on historical slope geological disaster data, several evaluation indicators are taken into consideration to assess the possibility of the natural environment of the slope resisting the occurrence of geological disasters in the area to be assessed; and based on the evaluation indicators, a slope geological disaster defense resilience assessment model for the pre-disaster prevention stage is established and optimized, and the optimized model is applied.

[0017] Preferably, in S3, the weights of the mountainous urban land space resilience assessment indicators in the disaster emergency response and post-disaster recovery stages are determined based on the data in S1; and by inputting the dynamic resilience data of the disaster emergency response and post-disaster recovery stages in different years, the dynamic assessment results of the land space resilience in the disaster emergency response and post-disaster recovery stages are obtained.

[0018] Preferably, in S3, the interaction between land space development and utilization and geological disasters is taken into consideration, and land space resilience assessment indicators for mountainous towns in the disaster emergency response and post-disaster recovery stages are selected to evaluate the disaster emergency response and post-disaster recovery capabilities.

[0019] Preferably, the step of determining the weights of the mountainous urban land space resilience assessment indicators in the disaster emergency response and post-disaster recovery stages in S3 is specifically as follows:

[0020] S3.1: Based on the subjective weighting method, subjective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during emergency response and post-disaster recovery;

[0021] S3.2: Based on the objective weighting method, objective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during the emergency response and post-disaster recovery stages;

[0022] S3.3: Based on the combined weight analysis method, the subjective weight and objective weight are combined and weighted.

[0023] Preferably, in S4, based on the evaluation results of pre-disaster prevention resilience, emergency response resilience during disasters and post-disaster recovery resilience of each evaluation unit obtained in S2 and S3, the best and worst comprehensive resilience situations are calculated, and the comprehensive resilience of each evaluation unit is ranked to obtain the comprehensive resilience level of each evaluation unit in the study area.

[0024] Preferably, in S5, the weight of each indicator is determined according to the evaluation model of pre-disaster prevention resilience, disaster emergency response resilience and post-disaster recovery resilience of each evaluation unit obtained in S2-S3, the obstacle degree of each evaluation unit on each indicator is calculated, and a resilience improvement strategy is proposed based on the analysis results in S4.

[0025] Compared with the prior art, the present invention provides a dynamic assessment method for mountain land space defense resilience against geological disasters, which has the following beneficial effects:

[0026] The present invention divides slope geological disasters into different stages, and adopts appropriate evaluation methods and models such as machine learning to dynamically quantify and analyze the defense resilience of mountain urban systems in the face of slope geological disasters at different stages. It also optimizes the analysis to obtain the full-cycle comprehensive resilience of the study area in the face of slope geological disasters, and finally conducts an obstacle analysis to point out the weak links in the defense resilience of mountain urban land space, providing a reference for the land space planning of mountain urban areas, and is extremely practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the method mentioned in Example 1 of the present invention;

[0028] Figure 2 It is a schematic diagram of the results of the spatial defense resilience of mountainous urban land in the pre-disaster prevention stage mentioned in Example 2 of the present invention;

[0029] Figure 3 It is a schematic diagram of the results of the spatial defense resilience of mountainous urban land in the emergency response during a disaster and the post-disaster recovery stage mentioned in Example 2 of the present invention;

[0030] Figure 4 This is a schematic diagram of the comprehensive resilience results of mountainous urban land space for full-cycle defense against slope geological disasters mentioned in Example 2 of the present invention;

[0031] Figure 5This is a schematic diagram of the diagnostic results of the mountainous urban land space defense resilience obstacles for full-cycle defense against slope geological disasters in Banan District, Chongqing in 2020 mentioned in Example 2 of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0033] The present invention divides slope geological disasters into different stages, respectively adopts appropriate evaluation methods and models such as machine learning, dynamically quantifies and analyzes the defensive resilience of mountainous urban systems in the face of different stages of slope geological disasters, and optimizes and analyzes them to obtain the full-cycle comprehensive resilience of the study area in the face of slope geological disasters, and finally conducts an obstacle degree analysis to point out the weak links in the defensive resilience of mountainous urban land space, providing a reference basis for mountainous urban land space planning, and having extremely strong practicality. Specifically including the following contents.

[0034] Embodiment 1:

[0035] See also Figure 1 The present invention provides a dynamic assessment method for mountain land space defense resilience against geological disasters, comprising the following steps:

[0036] S1: Obtain historical slope geological disaster data and mountain town land space resilience assessment data in the study area, and establish a slope geological disaster defense resilience database in the study area; the details are as follows:

[0037] The slope geological disaster resilience database in the study area includes but is not limited to: constant or slowly changing high-precision digital elevation models (DEM), geological data, dynamically changing meteorological and hydrological data, economic development data, social indicator data, infrastructure data, etc.

[0038] S2: A machine learning algorithm is used to construct a slope geological disaster resilience assessment model for the pre-disaster prevention stage; the details are as follows:

[0039] The data obtained by S1 is used to optimize the training of the slope geological disaster defense resilience assessment model in the pre-disaster prevention stage. The data in the slope geological disaster defense resilience database of the study area obtained by S1 used in this step include but are not limited to: elevation, slope, slope aspect, slope position, slope aspect variability, slope variability, curvature, terrain undulation, micro-topography, surface roughness, elevation variation coefficient, surface cutting depth, distance from faults, distance from roads, distance from rivers, lithology, soil type, terrain moisture index, average annual rainfall, land use type, normalized vegetation index, etc.

[0040] Machine learning algorithms include but are not limited to: decision tree, random forest, support vector machine, artificial neural network, XGBoost, LightGBM and other models.

[0041] Methods for optimizing machine learning models include, but are not limited to: algorithm hyperparameter optimization, dominant factor screening, evaluation unit scale selection, sample extraction methods and sample ratio comparison, etc.

[0042] Then, an assessment of the spatial resilience of mountainous urban areas is conducted in the pre-disaster stage. Specifically, based on historical slope geological disaster data and taking into account several evaluation indicators, the possibility of the natural environment of the slope resisting geological disasters in the area to be assessed is assessed. Based on the evaluation indicators, a slope geological disaster defense resilience assessment model for the pre-disaster prevention stage is established and optimized, and the optimized model is applied.

[0043] By inputting dynamic data such as rainfall in different years, land use types, and human activities, we can obtain dynamic assessment results of the national space defense resilience in the pre-disaster prevention stage.

[0044] S3: A combined weighting method combining subjective and objective factors is used to construct a slope geological disaster resilience assessment model for emergency response and post-disaster recovery stages; the details are as follows:

[0045] Based on the data in S1, the weights of the mountainous urban land space resilience assessment indicators in the emergency response and post-disaster recovery stages are determined; considering the interaction between land space development and utilization and geological disasters, the mountainous urban land space resilience assessment indicators in the emergency response and post-disaster recovery stages are selected to evaluate the emergency response and post-disaster recovery capabilities. The dynamic data in the slope geological disaster defense resilience database of the study area obtained by S1 include but are not limited to: green coverage rate, per capita green area, urbanization rate, per capita urban construction land area, per capita road length, per capita water supply pipeline length, per capita gas pipeline length, population density, proportion of old and young population, fixed asset investment, per capita disposable income of residents, per capita GDP, education expenditure, social security and employment expenditure, medical and health expenditure, number of medical and health institutions, number of employees in health institutions, number of disaster prevention and mitigation science activities, and community neighborhood committee density.

[0046] The specific steps for determining the weights of the mountainous urban land space resilience assessment indicators during the emergency response and post-disaster recovery stages are as follows:

[0047] S3.1: Based on the subjective weighting method, subjective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during emergency response and post-disaster recovery;

[0048] S3.2: Based on the objective weighting method, objective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during the emergency response and post-disaster recovery stages;

[0049] S3.3: Based on the combined weight analysis method, the subjective weight and objective weight are combined and weighted.

[0050] Methods for implementing subjective weighting include but are not limited to: analytic hierarchy process (AHP), expert survey method (Delphi method), and year-on-year scoring method. Methods for implementing objective weighting include but are not limited to: entropy weight method, CRITIC method, information weight method, and independence weight method. Methods for implementing combined weight analysis include but are not limited to: additive synthesis method, multiplicative synthesis method, range maximization, matrix thinking, distance function method, and game theory method.

[0051] By inputting dynamic data such as economic indicators and social indicators from different years, we can obtain dynamic assessment results of the resilience of the national space during the emergency response and post-disaster recovery stages.

[0052] S4: The comprehensive resilience of the study area is evaluated using a multi-attribute decision-making comprehensive evaluation method; the details are as follows:

[0053] The comprehensive evaluation methods used include but are not limited to: TOPSIS, grey correlation method, rank sum ratio method (RSR), and VIKOR method. According to the evaluation results of pre-disaster prevention resilience, emergency response resilience during disasters, and post-disaster recovery resilience of each evaluation unit obtained by S2 and S3, the best and worst comprehensive resilience are calculated, and the comprehensive resilience of each evaluation unit is ranked. The resilience level classification method can be selected according to the actual situation. Common methods include but are not limited to: expert experience method, natural breakpoint method, geometric interval, equal interval classification, and quantile. The comprehensive resilience level of each evaluation unit in the study area is obtained.

[0054] S5: Based on the evaluation and analysis results of S2-S4, a barrier model is constructed to diagnose the barrier degree of resilience, and a resilience improvement strategy is proposed based on the diagnosis results. The details are as follows:

[0055] According to the evaluation models of pre-disaster prevention resilience, disaster emergency response resilience, and post-disaster recovery resilience of each evaluation unit obtained in S2-S3, the weights of each indicator are determined. Methods for obtaining the weights of each evaluation indicator required to construct the barrier model include but are not limited to: geographic detector method, Spearman correlation analysis, SHAP machine learning interpretable analysis, and combined weight analysis. The barrier degree of each evaluation unit on each indicator is calculated, and a resilience improvement strategy is proposed based on the analysis results in S4. The higher the barrier degree, the greater the impact of the indicator on resilience.

[0056] Embodiment 2:

[0057] This embodiment is explained using the resilience assessment of Chongqing in 2020 as an example.

[0058] S1: Obtain historical slope geological disaster data and mountain town land space resilience evaluation data from 2010 to 2019 in Chongqing, import them into ArcGIS software for layer processing and reclassification, and establish a slope geological disaster defense resilience database in the study area.

[0059] The slope geological disaster resilience database established in the study area includes but is not limited to: constant or slowly changing high-precision digital elevation model (DEM), geological data, dynamically changing meteorological and hydrological data, economic data, and social indicator data. Reclassification and preprocessing using ArcGIS software can reduce data errors and improve the accuracy of resilience model assessment results. Based on the high-precision digital elevation model (DEM) of the study area, the ArcGIS software's perfect geospatial processing tools can maximize the accuracy and efficiency of the analysis.

[0060] S2: Based on the slope geological disaster resilience database data of Chongqing from 2010 to 2019 obtained in S1, the slope geological disaster resilience assessment model in the pre-disaster prevention stage is optimized and trained, and then the land space resilience assessment of mountainous towns in the pre-disaster stage is conducted. Dynamic data such as rainfall, land use type, NDVI, etc. in Chongqing in 2020 are input to obtain the land space resilience assessment results in the pre-disaster stage. Among them, the machine learning algorithm of this embodiment adopts the random forest algorithm, and the applicability and prediction performance of the random forest algorithm have been proven in the existing slope geological disaster susceptibility and hazard assessment research. Optimizing and training the machine learning model can maximize the performance of the model and improve the reliability and accuracy of the resilience evaluation. This embodiment optimizes the random forest parameters mtry and ntree in an iterative manner to determine their optimal values. The model prediction performance is obtained by comparing the two training sample selection methods of random selection and ten-fold cross validation, and the model with better effect is selected as the final evaluation result.

[0061] By using a reasonable threshold division method to divide the resilience evaluation structure of the study area into resilience levels, the spatial distribution of resilience in the entire area can be intuitively obtained. The pre-disaster emergency response resilience level division method in this embodiment adopts the natural breakpoint method in ArcGIS software. The pre-disaster prevention resilience level is divided into 5 categories by the natural breakpoint method, and the resilience levels are from high to low: high resilience, relatively high resilience, medium resilience, relatively low resilience, and low resilience. The resilience assessment results in the pre-disaster stage are as follows: Figure 2 shown.

[0062] S3: Use the dynamic data of Chongqing's slope geological disaster defense resilience database from 2010 to 2019 obtained in S1 to determine the weights of the mountainous urban land space resilience assessment indicators in the emergency response and post-disaster recovery stages. Input the dynamic data of Chongqing's relevant economic indicators, social indicators, etc. in 2020 to obtain the land space resilience assessment results in the emergency response and post-disaster recovery stages.

[0063] In this embodiment, the subjective weighting method uses the hierarchical analysis method, and the objective weighting method uses the entropy weight method. The main steps of implementing the hierarchical analysis method to determine the indicator weights include:

[0064] 1. Establish a hierarchical model;

[0065] 2. Construct a discriminant matrix and compare the relative importance of the criteria pairwise;

[0066] 3. Weight calculation, taking the approximate solution of the maximum eigenvector as the weight value of the judgment matrix;

[0067] 4.Consistency check.

[0068] The main steps to implement the entropy weight method to determine the indicator weights include:

[0069] 1. Data standardization.

[0070] 2. Calculate the proportion of each sample under each indicator and regard it as the probability used in the relative entropy calculation;

[0071] 3. Calculate the information entropy of each indicator, calculate the information utility value, and normalize it to obtain the entropy weight of each indicator.

[0072] Since the weights of the impact factors obtained by the hierarchical analysis method are influenced by expert experience and are highly subjective, there is a risk of subjective bias or artificial exclusion of certain indicators, which is not convincing. However, the use of the entropy weight method alone to determine the objective weights of the impact factors may ignore the practical significance of some impact factors and fail to take into account the value judgment of practical problems.

[0073] Therefore, this study adopts the multiplication synthesis method to combine the hierarchical analysis method and the entropy weight method for weighting. The calculation formula is shown as follows:

[0074]

[0075] Among them, w i is the combined weight; To obtain the subjective weights of resilience indicators through AHP; To obtain the objective weight of resilience index through entropy weight method.

[0076] By combining the subjective analytic hierarchy process with the objective entropy weight method, the subjective bias and objective bias of resilience evaluation are eliminated, and the actual situation of disaster resilience of mountain towns in the study area during emergency response and post-disaster recovery stages when facing slope geological disasters can be truly, objectively, completely and accurately reflected.

[0077] Each resilience assessment indicator is assigned a corresponding weight value, and then the resilience assessment results of mountainous urban land space in the emergency response and post-disaster recovery stages are obtained by superimposing each resilience assessment indicator, such as Figure 3 shown.

[0078] S4: The TOPSIS method is used as a comprehensive evaluation method to evaluate the comprehensive resilience of the study area. The main steps to implement the TOPSIS method to evaluate the comprehensive resilience of the study area include:

[0079] 1. The original matrix is ​​normalized to unify the research data into extremely large indicators;

[0080] 2. Forward matrix normalization;

[0081] 3. Calculate the distance between each evaluation object and the maximum value and the minimum value, calculate the score and normalize it;

[0082] 4. Sort the solutions by descending order based on the scores of the evaluation objects, and obtain the ranking results of the evaluation objects from best to worst.

[0083] In this embodiment, the comprehensive resilience evaluation results are divided into five levels according to the natural breakpoint method, namely high resilience, relatively high resilience, medium resilience, relatively low resilience and low resilience. Figure 4 shown.

[0084] S5: This embodiment uses the geographic detector method to normalize the factor detection results to determine the weight of each indicator, and then calculates the obstacle degree of each evaluation unit on each indicator. The higher the obstacle degree, the greater the impact of the indicator on resilience. Based on this, a resilience improvement strategy is proposed. Taking the obstacle degree diagnosis results of Banan District, Chongqing City in this embodiment as an example, Figure 5 These are the top five evaluation indicators of resilience barriers in Banan District. From the barrier diagnosis results, we can see that per capita GDP has the greatest impact on the resilience of the land space in Banan District, followed by the average annual rainfall.

[0085] In summary, this example uses ArcGIS software to extract the digital elevation model of the study area and preprocess the geographic information data to establish a slope geological disaster defense resilience database for the study area. Then, the machine learning algorithm random forest is used to construct a dynamic assessment model of mountain town land space resilience for the pre-disaster stage of slope geological disasters. Next, the dynamic assessment model of mountain town land space resilience in the disaster and post-disaster stages is constructed by the combined weighting method of analytic hierarchy process-entropy weight method. Then, the superior and inferior solution distance method is used to construct a comprehensive resilience model of mountain town land space. Finally, the geographic detector method is used to determine the weights of each indicator, and the obstacle model is constructed to form a dynamic assessment method for urban land space defense resilience for the whole cycle of slope geological disasters. This method can dynamically quantify and analyze the resilience of mountain town systems in the face of different stages of slope geological disasters, and optimize it to obtain the comprehensive resilience of the whole cycle of the study area in the face of slope geological disasters. Finally, the obstacle analysis is performed to point out the weak links of mountain town land space defense resilience, which provides a reference for mountain town land space planning and has strong practicality.

[0086] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A dynamic assessment method for mountain land space defense resilience against geological disasters, characterized in that: The steps include: S1: Obtain historical slope geological disaster data and mountain town land space resilience assessment data in the study area, and establish a slope geological disaster defense resilience database in the study area; S2: Use machine learning algorithms to build a slope geological disaster defense resilience assessment model for the pre-disaster prevention stage. Use the data obtained in S1 to optimize the training of the slope geological disaster defense resilience assessment model for the pre-disaster prevention stage, and then conduct a pre-disaster urban land space resilience assessment. By inputting the dynamic resilience data of the pre-disaster prevention stage in different years, the dynamic assessment results of the land space defense resilience in the pre-disaster prevention stage are obtained. In the process of assessing the spatial resilience of mountainous urban areas in the pre-disaster stage, based on historical slope geological disaster data, several evaluation indicators are considered to assess the potential of the natural environment of the slope to resist geological disasters in the area to be assessed; Based on the evaluation indicators, a slope geological disaster defense resilience assessment model for the pre-disaster prevention stage was established and optimized, and the optimized model was applied; S3: A combined weighting method combining subjective and objective factors is used to construct a slope geological disaster resilience assessment model for emergency response and post-disaster recovery stages; Determine the weights of the mountainous urban land space resilience assessment indicators in the disaster emergency response and post-disaster recovery stages based on the data in S1; obtain the dynamic assessment results of the land space resilience in the disaster emergency response and post-disaster recovery stages by inputting the dynamic resilience data in the disaster emergency response and post-disaster recovery stages in different years; Considering the interaction between land space development and utilization and geological disasters, we selected indicators for assessing the resilience of mountainous urban land space in the emergency response and post-disaster recovery stages, and conducted an assessment of the emergency response and post-disaster recovery capabilities. S4: Use multi-attribute decision-making comprehensive evaluation method to evaluate the comprehensive resilience of the study area; According to the evaluation results of pre-disaster prevention resilience, emergency response resilience during disasters, and post-disaster recovery resilience of each evaluation unit obtained by S2 and S3, the best and worst comprehensive resilience situations are calculated, and the comprehensive resilience of each evaluation unit is ranked to obtain the comprehensive resilience level of each evaluation unit in the study area; S5: construct an obstacle degree model based on the evaluation and analysis results of S2-S4, perform resilience obstacle degree diagnosis, and propose resilience improvement strategies based on the diagnosis results; According to the evaluation models of pre-disaster prevention resilience, disaster emergency response resilience and post-disaster recovery resilience of each evaluation unit obtained in S2-S3, the weight of each indicator is determined, the obstacle degree of each evaluation unit on each indicator is calculated, and a resilience improvement strategy is proposed based on the analysis results in S4.

2. According to the method of dynamic assessment of mountain land space defense resilience against geological disasters in claim 1, it is characterized in that: The specific steps for determining the weights of the mountainous urban land space resilience assessment indicators in the emergency response and post-disaster recovery stages in S3 are as follows: S3.1: Based on the subjective weighting method, subjective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during emergency response and post-disaster recovery; S3.2: Based on the objective weighting method, objective weights are assigned to the spatial resilience assessment indicators of mountainous urban areas during the emergency response and post-disaster recovery stages; S3.3: Based on the combined weight analysis method, the subjective weight and objective weight are combined and weighted.

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