Work

Advanced Landslide Detection Using Machine Learning and Remote Sensing Data

Year: 2024

Type: article

Abstract: Landslides can cause severe damage to infrastructure and human life, making early detection and warning systems critical for mitigating their impact. In this study, we propose a machine learning approach for landslide detection using remote sensing data and topographical features. We evaluate the performance of several machine learning algorithms, including Tree, Random Forest, Gradient Boosting, Logistic Regression, Naïve Bayes, AdaBoost, Neural Network, SGD, kNN, and SVM, on a dataset of remote sensing images and topographical features from the Sikkim region in Malaysia. The results show that the SVM algorithm outperforms the other algorithms with an accuracy of 96.7% and a F1 score of 0.97. The study demonstrates the potential of machine learning algorithms for landslide detection, which can help improve early warning systems and reduce the impact of landslides.

Source: Data & Metadata

DOI: 10.56294/dm2024.419

Authors: Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hatim Solayman Migdadi, Mutasem Alkhasawneh, Muhyeeddin Alqaraleh

Institutions: Jadara University, Hashemite University, Ajloun National University, Zarqa University

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4403516304

FWCI: 9.983

Cites: 45

Cited by: 24

Related to: 10