A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping

Sun, Dongmei; Shi, Shuxian; Wen, Haoran; Xu, Jiahui; Zhou, Xinzhi; Wu, Jianping

doi:10.1016/j.geomorph.2021.107623

Cited by 114 publications

(41 citation statements)

References 38 publications

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“…Based on comprehensive consideration of various factors and the actual conditions of the study area, 22 factors were selected as the initial conditioning factors including topography (elevation, slope, degree of relief, aspect, slope position, landforms, curvature, profile curvature, plan curvature, terrain roughness index (TRI), topographic wetness index (TWI), sediment transport index (STI), and stream power index (SPI)), geology (lithology, distance from fault, and combination reclassification of the stratum dip direction and slope aspect (CRDS)), environmental conditions (normalized vegetation index (NDVI), distance from hydrographic net, annual average rainfall, and land cover), and human activities (distance from roads and POI kernel density). All the 22 conditioning factors mentioned above were used to create the geospatial database with GIS software (Sun et al, 2021c).…”

Section: Geospatial Databasementioning

confidence: 99%

“…Wang et al (2021) used GeoDetector to identify spatial relationships among the influencing factors about soil heavy metal As. Sun et al (2021c) constructed the LSM model in Fengjie County by selected fundamental influencing factors, which are screened by GeoDetector and recursive feature elimination. Although the logistic regression model has a large number of applications in the field of LSM, due to the limitation of the model itself, it has limitations in dealing with a large number of conditioning factors with multicollinearity (Sun et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

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Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Sun

Wen

et al. 2021

Front. Earth Sci.

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This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

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Section: Geospatial Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Sun

Wen

et al. 2021

Front. Earth Sci.

Self Cite

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“…It overcomes the low efficiency problems associated with local downloads, storage, and preprocessing and uses Google's powerful computing capabilities to analyze and process a variety of environmental and social data [16]. With these advantages, GEE has been widely used to map land cover types and associated changes over large areas [41], perform data fusion [42,43], act as a geodetector [44,45], and investigate ecological environments [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Spatial pattern of the ecological environment in Yunnan Province

Wang

Ding

2021

PLoS ONE

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Ecological science focuses on the structure and function of the natural environment. However, the study of ecological environments primarily focuses on single-element research and lacks a comprehensive perspective. To examine ecological environmental trends on different scales, the present paper selected Yunnan Province as the study area. Chemical oxygen demand, rocky desertification, forest coverage, natural disaster data and spatial analysis methods were used to obtain the ecological environmental characteristics of each county and construct a comprehensive evaluation method of the ecological environment. The present paper revealed that the environmental capacity in Yunnan Province was at a moderate level, the ecological environment fragility was remarkable, the significance of the ecological environment was very high, natural disasters occurred frequently, and spatial differentiation between ecological environments was obvious. The province may be divided into three functional areas: the comprehensive-balanced area, the efficiency-dominated area and the environment-dominated area. Central Yunnan was a key development zone and the main area for the manufacturing and service industries, which were built as a modern industrial system in Yunnan Province. The ecological environment in northwestern Yunnan and southern Yunnan is of high significance, and this region was an ecological environment protection area that was important area for the construction of the modern agricultural system in Yunnan Province. To achieve sustainable development of the ecological environment, the spatial characteristics of the ecological environment must be determined at the county scale.

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“…A number of landslide susceptibility related analyses were developed in the selected region due to the large amount of landslide and their potential threat to local habitations [38][39][40][41][42][43][44][45]. Most of them mainly focused on enhancing the performance of predictive model, by using hybrid machine learning models and hyper-parameter optimization methods, while neglecting the practical understanding and applications of landslide susceptibility result.…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

Liu

Yin

et al. 2021

Remote Sensing

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The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation.

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A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping

Cited by 114 publications

References 38 publications

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Spatial pattern of the ecological environment in Yunnan Province

Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

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