Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran

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.

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Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran

Cited by 3 publications

References 91 publications

Developing a hybrid deep learning model with explainable artificial intelligence (XAI) for enhanced landslide susceptibility modeling and management

Developing a hybrid deep learning model with explainable artificial intelligence (XAI) for enhanced landslide susceptibility modeling and management

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Advanced Landslide Detection Using Machine Learning and Remote Sensing Data

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