Abstract
Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.
Highlights
Catastrophic landslides in mountainous terrains interact with human environment and cause adverse impacts on lives and properties [1]
Data driven methods are extensively used for Landslide Susceptibility Mapping (LSM), and the earlier statistical methods using Geographical Information System (GIS)-based approaches are being replaced by advanced Machine Learning (ML) algorithms
The influence of the choice of the ML algorithm, sampling strategies, and data splitting for LSM is evaluated in detail using a case study from the Wayanad district in Kerala
Summary
Catastrophic landslides in mountainous terrains interact with human environment and cause adverse impacts on lives and properties [1]. Aids for managing the risk due to landslides is a topic of which several decades of research has been devoted [2,3]. Mapping the spatial distribution of landslide hazard is one of the most-adopted strategies for risk management, as the landslide susceptibility maps can be used by the government for strategic planning and development [4]. Learning (ML) techniques and computational facilities, Landslide Susceptibility Mapping (LSM) have become much easier. Data driven methods are extensively used for LSM, and the earlier statistical methods using Geographical Information System (GIS)-based approaches are being replaced by advanced ML algorithms. Five different algorithms are considered in this study, viz., Naïve Bayes (NB), Logistic Regression (LR), K Nearest Neighbors (KNN), Random
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