Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

Xu, Kai; Guo, Qing; Xiao, Jie; Qin, Yanshan; Chen, Dan; Kong, Chunfang

doi:10.1080/13658816.2014.992436

Cited by 31 publications

(15 citation statements)

References 14 publications

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“…Various algorithms for ANN have been proposed in previous literature, among which the BPNN model is the most widely used [30,31]. The network has an enhanced functionality of highly nonlinear correspondent relations that analyze input and output and fine feasibility.…”

Section: Bpnnmentioning

confidence: 99%

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Liao¹,

Liao

2020

Open Geosciences

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Slope stability assessment is a critical concern in construction projects. This study explores the use of multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters that are associated with the evaluation of slope stability. A comparative study of machine learning solutions for slope stability assessment that relied on backpropagation neural network (BPNN) and MARS was conducted. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Research results suggest that BPNN and MARS models can model the relationship between the safety factor and the slope parameters. Also, the MARS model has the advantages of computational efficiency and easy interpretation.

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Section: Bpnnmentioning

confidence: 99%

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Liao¹,

Liao

2020

Open Geosciences

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“…With the development of computing power, geospatial data, and technologies, a growing number of studies have explored the potential of using machine learning (ML) based algorithms with geographic information system (GIS) datasets and remote sensing images for constructing landslide susceptibility models on the regional scale, such as decision trees (DT) [8,9,10], entropy- and evolution-based algorithms [11,12,13,14], fuzzy theory [15,16,17], neural network [12,18,19,20,21,22], neural-fuzzy systems [23,24], and support vector machines [20,21]. Random forests (RF) has received increased attention in the ML domain in recent years because of the following advantages: (1) Excellent accuracy [25], (2) processing speed [26,27], (3) few parameter settings [28,29], (4) availability of high-dimensional data analysis (e.g., hyperspectral image cubes) [30,31], and (5) insensitivity to imbalanced training data [32].…”

Section: Introductionmentioning

confidence: 99%

Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning

Lai

Tsai

2019

Sensors

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This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses.

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“…Variations of lithology lead to different resistances to erosion processes, due to varied characteristics, such as different composition, structure, and compactness [38][39][40]. The high storage capacity of aquifers can increase soil moisture and soften the structural plane of slopes, leading to a reduction in the original stability.…”

Section: Lithology Aquifer Storage Capacity Precipitation and Landmentioning

confidence: 99%

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

Chen

Han

Huang

et al. 2017

IJGI

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A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault); landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP). On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions.

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Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

Cited by 31 publications

References 14 publications

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

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