Characterizing landslides in a tectonically active regime like the Himalaya is challenging, particularly in the Dibang Valley of northeast (NE) India, where complex geology, erratic rainfall, steep terrain, slope instability, and recent anthropogenic activities enhance the risk vulnerability. While landslide susceptibility has been widely studied worldwide, it has been studied on a limited scale in NE India despite high vulnerability. To address this gap, an attempt has been made to explore the landslide susceptibility in the region using a machine learning-based framework comprising eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to simulate the influence of multiple conditioning factors. Both XGBoost and LightGBM demonstrated high predictive performance (AUC ~ 0.96) and predicted maximum landslide susceptibility of 25.48% (XGBoost) to 35% (LightGBM), respectively, in the area. The region around the NH-313 road section along the Dibang River and settlements around Anini, Punli, and Etalin are highly vulnerable. The analysis is further supported by a comprehensive landslide inventory derived from multisource datasets, which highlights an increase in soil moisture content during the monsoon period, thereby affecting slope stability. To determine the contribution of individual conditioning factors in the model prediction, the SHapley Additive ExPlanations (SHAP) method was employed. The results suggested that the geospatial and hydro-meteorological variables, including elevation, lithology, lineament density, and rainfall, significantly influence the predicted estimates. The methodology adopted here is robust in nature, and findings support the need for risk management, early warning systems, and hazard mitigation strategies on a long-term basis, especially given the impact of infrastructure projects like the Etalin Hydropower Project on the spatial and temporal dynamics of landslides in the region.
Machine Learning-based Landslide Susceptibility Modeling in the Dibang Valley, NE India / Mihu, Shiv; Tomar, Kritagya Kumar Singh; Kumar, Ankush; Choudhari, Panduranga Prakash; Raju, Ashwani; Gentilucci, Matteo; Barbieri, Maurizio; Kumar, Pankaj; Rongpi, Rumi. - In: EARTH SYSTEMS AND ENVIRONMENT. - ISSN 2509-9426. - (2026). [10.1007/s41748-026-01036-3]
Machine Learning-based Landslide Susceptibility Modeling in the Dibang Valley, NE India
Barbieri, Maurizio
Membro del Collaboration Group
;
2026
Abstract
Characterizing landslides in a tectonically active regime like the Himalaya is challenging, particularly in the Dibang Valley of northeast (NE) India, where complex geology, erratic rainfall, steep terrain, slope instability, and recent anthropogenic activities enhance the risk vulnerability. While landslide susceptibility has been widely studied worldwide, it has been studied on a limited scale in NE India despite high vulnerability. To address this gap, an attempt has been made to explore the landslide susceptibility in the region using a machine learning-based framework comprising eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to simulate the influence of multiple conditioning factors. Both XGBoost and LightGBM demonstrated high predictive performance (AUC ~ 0.96) and predicted maximum landslide susceptibility of 25.48% (XGBoost) to 35% (LightGBM), respectively, in the area. The region around the NH-313 road section along the Dibang River and settlements around Anini, Punli, and Etalin are highly vulnerable. The analysis is further supported by a comprehensive landslide inventory derived from multisource datasets, which highlights an increase in soil moisture content during the monsoon period, thereby affecting slope stability. To determine the contribution of individual conditioning factors in the model prediction, the SHapley Additive ExPlanations (SHAP) method was employed. The results suggested that the geospatial and hydro-meteorological variables, including elevation, lithology, lineament density, and rainfall, significantly influence the predicted estimates. The methodology adopted here is robust in nature, and findings support the need for risk management, early warning systems, and hazard mitigation strategies on a long-term basis, especially given the impact of infrastructure projects like the Etalin Hydropower Project on the spatial and temporal dynamics of landslides in the region.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
