<![CDATA[An increasing demand for precise forecasts concerning the likelihood of landslides served as the impetus for this investigation. Human life, infrastructure, and the environment are all profoundly affected by this natural occasion. Constructing models capable of discerning intricate patterns among diverse factors that impact the likelihood of landslide occurrences constitutes the primary obstacle in landslide detection. Predicting potential landslides requires algorithms that are both accurate and efficient in their processing of vast quantities of data encompassing a variety of geographical, environmental, and ecological characteristics. An evaluation of the efficacy of both Gradient Boosting Machine and Light Gradient Boosting Machine in identifying patterns associated with landslides is accomplished by comparing their performance on a large and complex dataset. In the realm of potential landslide detection, the primary aim of this research endeavor is to assess the predictive precision, computation duration, and generalizability of Gradient Boosting Machine and Light Gradient Boosting Machine. This research aims to enhance comprehension regarding the comparative benefits of these two approaches in surmounting the obstacles associated with risk assessment and modeling pertaining to potential landslides, with a specific emphasis on efficiency and precision. The research findings are anticipated to serve as a valuable reference in the identification of more efficient approaches to reduce the likelihood of landslide-induced natural catastrophes. The accuracy of the GBM experiment reached 82% and LGBM reached 81%.]]>
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