Geospatial Project: Landslide Prediction

This literature review examines the use of machine learning (ML) algorithms for landslide identification and provides an overview of recent studies in this field. The most used algorithms for landslide identification include support vector machine (SVM), decision trees, random forests, artificial neural networks (ANNs), and deep learning models such as convolutional neural networks (CNN). The review highlights the strengths and limitations of these approaches, such as data scarcity, imbalanced datasets, and interpretability issues, and proposes solutions to these challenges. Two specific studies in landslide prediction using ML are discussed, including a digital inventory using supervised learning to detect landslides and an optimized random forest model to evaluate landslide susceptibility with 16 conditioning factors. The review concludes by emphasizing the potential of ML techniques for landslide identification and the importance of understanding their strengths and limitations. This paper provides valuable insights for researchers and practitioners interested in applying ML algorithms for landslide identification and prediction.