Proceedings Article | 19 October 2023
KEYWORDS: Network landslides, Machine learning, Lawrencium, Support vector machines, Data modeling, 3D modeling, Receivers, Education and training, Algorithm development, Matrices
The Uttarakhand state of India is highly prone to landslides causing valuable loss of life and money frequently and hence its careful study is of utmost importance. Recently several types of machine learning algorithms have been developed and applied for producing landslide susceptibility maps in various world regions. In this study landslide susceptibility assessment was undertaken in landslide prone areas of Uttarakhand state (India) applying three Machine learning algorithms (a) Support Vector Machines (SVM), (b) Logistic Regression (LR), and (c) Multilayer Perceptron (MLP). The comparative performance of these methods has been evaluated using various statistical index-based methods. In the development of the model, several important landslides affecting factors related to geomorphology, geology, and geo-environment such as slope angles, elevation, slope aspects, curvatures, rainfall, distance to faults, distance to roads, distance to rivers, land use, and land cover, DSM, DTM, etc. have been identified and their relative importance has been explored. Models developed are trained for various locations spanning the major landslide locations of Uttarakhand including Sonprayag, Sitapur, Rampur, Kalimah, Madhya Maheshwar, Chamoli and Uttarkashi. Analysis of the results reveals that all the above-mentioned landslide models performed well for landslide susceptibility assessments. Further, it has been observed that the deep learning-based MLP multilayer perceptron model performs better than SVM and LR - models owing to its higher number of hidden layers. Several hyperparameter tuning studies have also been conducted to finetune the model.
