Landslide susceptibility studies in Uttarakhand using novel machine learning-based model

Ashok Anand; Arya Prakash Padhi; Alok Bhardwaj

doi:10.1117/12.2684332

19 October 2023 Landslide susceptibility studies in Uttarakhand using novel machine learning-based model

Ashok Anand, Arya Prakash Padhi, Alok Bhardwaj

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 1273315 (2023) https://doi.org/10.1117/12.2684332
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Conference Poster

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ashok Anand, Arya Prakash Padhi, and Alok Bhardwaj "Landslide susceptibility studies in Uttarakhand using novel machine learning-based model", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 1273315 (19 October 2023); https://doi.org/10.1117/12.2684332

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