Kerala is the third most densely populated state in India, with 860 persons per square kilometer. The uniqueness and diversity of the state’s topology make it highly vulnerable to natural hazards. Kerala State Emergency Operations Centre Kerala State Disaster Management Authority (2016). This study was initiated in the backdrop of landslides and floods in 2018, which had wreaked havoc in the region. Among the 4728 landslides reported in the state’s ten districts, Idukki was the worst affected with 2219 landslide occurrences. A statistically significant cluster of landslide hotspots was identified within the Idukki district using Getis-Ord Gi* statistics. Landslide susceptibility analysis was carried out using logistic regression (LR) and artificial neural network (ANN). Natural parameters influencing landslides such as slope, elevation, rainfall, geology, distance to drainage, and anthropogenic conditioning factors such as land use, road density, and quarry density were considered in this study. The results indicate that both natural and anthropogenic conditioning factors have a significant influence on landslide occurrences. According to the LR results, about 37.87% and 38.07% of the district’s total area is situated in high and medium landslide susceptibility zones. The results establish that ANN has better predictive performance compared with LR.

喀拉拉邦是印度第三大人口稠密的州，每平方公里860人。该州拓扑结构的独特性和多样性使其非常容易受到自然灾害的影响。喀拉拉邦紧急行动中心喀拉拉邦灾害管理局（2016）。这项研究是在2018年发生山体滑坡和洪水的背景下发起的。在该州的十个地区报告的4728个滑坡中，伊杜基受灾最严重，发生2219次滑坡。使用Getis-Ord Gi *统计数据，在Idukki地区确定了具有统计意义的滑坡热点聚类。使用逻辑回归（LR）和人工神经网络（ANN）进行了滑坡敏感性分析。影响滑坡的自然参数，例如坡度，海拔，降雨，这项研究考虑了地质，排水距离和人为条件因素，例如土地使用，道路密度和采石场密度。结果表明，自然因素和人为因素都对滑坡发生有重要影响。根据LR结果，该地区总面积的约37.87％和38.07％位于高和中滑坡敏感性区。结果表明，与LR相比，ANN具有更好的预测性能。该地区总面积的07％位于高和中等滑坡敏感性区。结果表明，与LR相比，ANN具有更好的预测性能。该地区总面积的07％位于高和中等滑坡敏感性区。结果表明，与LR相比，ANN具有更好的预测性能。