Predicting Landslide Using Machine Learning Techniques
Year: 2024
Type: conference-paper
Abstract: In mountainous areas prone to landslides, it’s crucial to map out where these hazardous events are likely to occur to mitigate risks effectively. This study focuses employing an integrated approach to assess landslide susceptibility using Random Forest (RF), Stacking, Vote, AdaBoostM1, and Bagging. 13 factors influencing landslide occurrence are identified for modeling purposes. To evaluate and compare the models’ performance, multiple statistical methods are employed. The analysis highlights the effectiveness of employing machine learning models, Random Forest (RF), Stacking, Bagging, and Vote methods. The results demonstrate the efficiency of the models in accurately predicting landslide susceptibility. The study suggests that similar hybrid models can be effectively utilized in other sensitive regions with comparable geo-environmental conditions for landslide susceptibility studies. By integrating various techniques and leveraging ensemble algorithms, these models offer improved accuracy and reliability in assessing landslide hazards. This comprehensive approach provides valuable insights for disaster management and risk reduction efforts in landslideprone areas worldwide.
Source: ITM Web of Conferences
Authors: Mehul Patel, Mittal Chavda, Rajesh Patel, Ankur Goswami, Jayesh Mevada
Institution: Sankalchand Patel University
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: diamond
OpenAlex ID: W4400677349