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Geospatial Analysis of Landslide Susceptibility Through Machine Learning In Relation to Environmental Indicators Across Global Regions

Geospatial Analysis of Landslide Susceptibility Through Machine Learning In Relation to Environmental Indicators Across Global Regions

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Arjun Nair 

Abstract

Landslides cause over a billion dollars per year in damages and the situation is only exacerbated by climate change. Landslide forecasting, as a result, is key for detecting these disasters. Traditional landslide prediction models often rely on localized data and extensive computational resources, limiting their applicability on a global scale and therefore have an accuracy rate of around 30%. Addressing this gap, our research leverages a dataset which uses an enhanced version of NASA’s Global Landslide Catalog. The dataset spans from 2010-2020, containing 10,100 landslide events with meteorological and geological factors preceding the event. After identifying key drivers of landslides and the minimum slope requirements for different lithologies, a Random Forest model was created using python to predict landslides based on their severity index. An 81% accuracy rate was found when trained and tested, proving to be much more effective when compared to traditional models. The model can now be used to provide a novel, data-driven perspective on landslide prediction allowing for a quicker evacuation of areas prone to landslides and providing enough time for countermeasures to be employed.

DOI

https://doi.org/10.31223/X5471F

Subjects

Physical Sciences and Mathematics

Keywords

Global Landslide Catalog, python, landslide severity index, Random Forest

Dates

Published: 2024-10-30 11:17

Last Updated: 2024-10-30 18:17

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No Creative Commons license

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Conflict of interest statement:
None