Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Abstract

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

“…It is observed in many studies that this additional step positively impacts the accuracy and performance of ML models. For example, studies like [45][46][47] have used Chi-square attribute evaluation (CSAE) and multicollinearity analysis by applying variance inflation factor (VIF) and tolerance to select the LCFs with the most influence on landslide. Researchers also have used the Ridge regression method for LCFs importance analysis [45].…”

“…For example, studies like [45][46][47] have used Chi-square attribute evaluation (CSAE) and multicollinearity analysis by applying variance inflation factor (VIF) and tolerance to select the LCFs with the most influence on landslide. Researchers also have used the Ridge regression method for LCFs importance analysis [45]. Other popular methods for feature selection and analysis includes relative risk regression analyse [45], fractal analysis [48], resampling scheme analysis and Pearson's correlation analysis [49], correlation-based features selections (CFS) [50], frequency ratio (FR) [51], fuzzy and weights of LCFs using SVM [52], principal component analysis (PCA) to select independent and significant LCFs [53], information gain method [54], GeoDetector and recursive feature elimination (RFE) method for LCFs optimization to reduce redundancy [51], interactive detector [51], one rule (one-R) [42], correlation attributes evaluation (CAE) where greater calculated average merit (AM) indicates more influence of the LCF [55], sensitivity analysis [56], Spearman's rank correlation coefficient [57], relief-F method [58], Fischer score analysis [47], and gain ratio method [59].…”

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

“…The secondary data source was derived from historical landslide information and satellite imagery between the years 2000 and 2020. As the landslide occurred in an inaccessible area, high-resolution Google Earth images were used to detect the landslide location [31]. In this study area, 70 landslide events were recorded.…”

“…The data-driven models are considered a quantitative method and have been proven to be an incredibly useful tool for landslide mapping [31]. Landslide prediction using data-driven models is able to estimate the possibility of a landslide by analyzing and interpreting the relationship between historical landslide events and various conditioning factors without using physical processes [32].…”

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

“…These developments have significantly improved LSM accuracy and efficiency. In previous research studies several machine learning-based methods such as supervised learning (SL) and unsupervised learning (USL) algorithms have been applied and compared in LSM in different regions (Chowdhuri et al, 2021a;Mehrabi and Moayedi, 2021;Saha et al, 2021;Solanki et al, 2022). Nevertheless, none of them can be applicable and effective in all cases.…”