A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District, China

Abstract

The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a datadriven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions.

Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques

“…Entropy is the measurement to a system disorder, instability, imbalance and uncertainty, etc. (Chen et al 2018). Entropy system works on the principle of Boltzmann where, entropy of the system is directly related to the degree of disorder.…”

“…The observed LSI values vary from 0.001 to 1.00. The output of RF-IOE model is classified into four classes low, medium, high and very high using natural jenk classification method (Figure 9(c)) (Pourghasemi et al 2012;Kaur et al 2017bKaur et al , 2018Chen et al 2018). The final output of RF-IOE predicts that 16.34%, 32.08%, 30.73% and 20.84% of the total area is under very high, high, medium and low susceptible zone, respectively.…”

“…There are various reported studies on comparative study of an expert knowledge-based model and data-driven models for landslide susceptibility mapping (Zhu et al 2014(Zhu et al , 2018Kavzolu, Colkesen, and Sahin 2019). Quantitative analysis or statistical models determine weight and rank based on logical tools but these statistical models require more data to generate a reliable result (Tien Bui et al 2011;Kaur, Gupta, and Parkash 2017a;Chen et al 2018). To overcome this issue, machinelearning techniques such as decision trees (Dietterich 2000;Althuwaynee et al 2014;Hong et al 2018), neurofuzzy (Pradhan, Lee, and Buchroithner 2010), support vector machines (Chen et al 2017a;Tien Bui et al 2017), boosted regression trees (Youssef et al 2016;Lee et al 2017), kernel logistic regression (Tien Bui et al 2016;Chen et al 2017b), naive bayes (Tsangaratos and Ilia 2016;Tien Bui et al 2017) and random forests (RF) (Pourghasemi and Kerle 2016;Kim et al 2018;Zhang et al 2017;Chen et al 2018) are widely used throughout the globe.…”

“…To overcome this issue, machinelearning techniques such as decision trees (Dietterich 2000;Althuwaynee et al 2014;Hong et al 2018), neurofuzzy (Pradhan, Lee, and Buchroithner 2010), support vector machines (Chen et al 2017a;Tien Bui et al 2017), boosted regression trees (Youssef et al 2016;Lee et al 2017), kernel logistic regression (Tien Bui et al 2016;Chen et al 2017b), naive bayes (Tsangaratos and Ilia 2016;Tien Bui et al 2017) and random forests (RF) (Pourghasemi and Kerle 2016;Kim et al 2018;Zhang et al 2017;Chen et al 2018) are widely used throughout the globe. In recent year's, comparison between different models are being widely used to determine the best techniques for landslide susceptibility zonation (Ciurleo, Cascini, and Calvello 2017;Chen et al 2018). According to Chen et al (2018), hybrid approach by integrating two or more models such as artificial neural network combined with fuzzy logic (Kanungo et al 2009), adaptive neuro-fuzzy inference (Dehnavi et al 2015), ANFIS integrated with combined FR (Chen et al 2017), random forest combined with statistical index (Chen et al 2018), random forest with index of entropy (Chen et al 2018) gives a better result with reasonable conclusion.…”

“…In recent year's, comparison between different models are being widely used to determine the best techniques for landslide susceptibility zonation (Ciurleo, Cascini, and Calvello 2017;Chen et al 2018). According to Chen et al (2018), hybrid approach by integrating two or more models such as artificial neural network combined with fuzzy logic (Kanungo et al 2009), adaptive neuro-fuzzy inference (Dehnavi et al 2015), ANFIS integrated with combined FR (Chen et al 2017), random forest combined with statistical index (Chen et al 2018), random forest with index of entropy (Chen et al 2018) gives a better result with reasonable conclusion. Performance of such models is usually assessed with receiver operating characteristic (ROC) curves, success and prediction rate curves (SPRC) and area under curve (AUC) values.…”