A Systematic Review of Landslides prediction mechanisms and analysis of Landslides in Western Ghats in Kerala and Maharashtra

Due to the intensity and magnitude environmental conditions are changed this is directly implicate the society. This sources significant damage to both natural ecosystem as well as manmade throughout numerous regions. Also, landslides can cause damage to property and infrastructure, disrupt transportation and communication systems, and even result in loss of life. The damage severity depends on various factors, such as the size and speed of the landslide, the type of material involved, and the location and vulnerability. This review paper focused on landslides in the region of Kerala and Maharashtra from Western Ghats (WGs) of two states in southwestern India. The article provided an overview of the geological, climate, and socio-economic factors that contribute to landslides in the region and the effects of landslides on the environment and local communities. Further Kerala is situated in Stable Continental Region (SCR). Therefore, tectonic and geological features that are contribute to seismic activity of SCR and specially in the perspective of Kerala state. The paper also discussed the area’s current efforts and strategies for landslide mitigation and management, including early warning systems and land-use planning. And it also emphasized the importance of learning from past landslides and applying the lessons learned for decreasing future disasters risks from the WGs region. This paper also examined recent advances in machine learning techniques for landslide prediction, early warning, and risk assessment, including artificial neural networks (ANNs), Random Forest (RF), and support vector machines (SVMs). Then, summarize the findings from the suitable literatures particularly highlight the performance of slopes in SCR under landslide conditions. This includes risk assessment methodologies and highly recommended forecasting mechanisms to address the solutions. The review highlighted the need for an integrated approach to landslide prevention and mitigation, which involves using machine learning techniques in combination with traditional methods. Also, it recognize the gaps in recent assessment model and take the solution for improving the gaps. Then, introduce the possible avenues for further analyses that could improve the landslide prevention techniques. Finally, the paper concluded with recommendations for future research and interventions to reduce the risk of landslides in the WGs by utilizing machine learning for landslide prevention in the WGs.