Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya

Over the years, landslide has become one of the most destructive events that can happen in hilly areas. Tehri, a region in the Himalayas is no different. Current research aids in the construction of ensemble models of DLNN and SVM, which are then compared with various SVM kernels. Landslide susceptibility mapping in the Tehri region of the Himalayas has been worked upon using a deep learning (DLNN), four machine learnings (SVM-RBF, SVM-Linear, SVM-Polynomial, SVM-Sigmoid), and their novel ensembles i.e., DLNN with SVM-RBF, DLNN with SVM-Linear, DLNN with SVM-Polynomial and DLNN with SVM-Sigmoid. 16 geo-environmental landslide conditioning factors (LCFs) have been considered. These models were trained using 70% of inventory landslides and tested using 30% of the same. The results revealed the superiority of DLNN, DLNN-SVM (RBF), DLNN-SVM (Linear) models which quantified 28.32, 26.96 and 22.41% of the area highly susceptible for landslide, respectively

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

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