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 landslides 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.
Highlights
In mountainous regions, landslides are regarded as one of the reoccurring natural hazards affecting human property and lives
The results showed that deep learning (DL) models outperformed tree Machine learning (ML) models in this analysis
normalised difference vegetation index (NDVI) and land use/land cover (LULC) were found to have greatly contributed to making an area prone to landslide based on RR
Summary
Landslides are regarded as one of the reoccurring natural hazards affecting human property and lives. Landside susceptibility (LS) indicates the spatial probability of landslides in an area [1]. The effect of landslides can change the topographic characteristic, forest, soil properties (consistence, structure, density, temperature, etc.), road and farming land, depending on the magnitude of landslides [2]. Landslides in Indian Himalayas account for over 14% of the global landslides as per Froude and Petley [3] database, and one of the understudied regions is Sikkim, despite it having a huge landslide problem. Dikshit et al [4] revealed that only 10% of landslide studies are conducted in Sikkim, of total studies across the Indian Himalayan states. The study area has witnessed destructive landslide events between 2007 and 2015 (https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg, accessed on 20 December 2020)
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