Muhammad Shareef Shazil

Machine Learning (ML) and Deep Learning (DL) techniques play a crucial role in understanding and predicting changes related to landslide dams. These advanced computational methods offer the capability to analyze large datasets and complex relationships within hydrogeomorphological systems, providing insights that traditional approaches may struggle to uncover. ML algorithms can be trained to recognize patterns in historical landslide data, terrain characteristics, and environmental variables to forecast potential landslide occurrences and dam formation. DL, a subset of ML that utilizes neural networks with multiple layers, further enhances the predictive power by automatically extracting intricate features from raw data. In the context of landslide dams, ML and DL models can aid in early warning systems, risk assessment, and mitigation strategies by identifying vulnerable areas, evaluating the stability of slopes, and predicting the behavior of landslide-induced dam formations. By harnessing the capabilities of ML and DL, researchers and practitioners can improve our understanding of landslide dynamics and enhance disaster preparedness and response efforts in regions prone to such hazards.