Machine Learning–Driven Landslide Nowcasting for Operational Early Warning in the Himalayan Region

The intensification of extreme rainfall has resulted in widespread landslide hazards in mountainous regions of the world. The Indian Himalayan Region, one of the most densely urbanized, has been facing an alarming increase in landslides, the prediction of which is difficult using existing empirical rainfall thresholds. This study develops a novel machine learning-driven landslide nowcasting system by integrating the landslide susceptibility (LSM) and probability of rainfall-induced landslides (P-RIL). The LSM provides the spatial location of future landslides by analyzing the terrain characteristics, anthropogenic factors, hydrological presence, and geological formations using the random forest (RF) method based on landslides occurring between 2017-2024. The results indicated that 7% of the area was under high susceptibility, followed by 12% under high susceptibility. To calculate the effect of rainfall in triggering landslides, the P-RIL was calculated considering R1 (rainfall on 1st day of occurrence), R3 (rainfall on 3rd day), R7 (7th day rainfall), R15 (15th day rainfall), Wetdays, Max_72 Hours, and antecedent rainfall index (ARI) as variables to train in the RF model. Finally, each day nowcasting results were obtained by integrating the LSM and P-RIL within a probabilistic framework. The landslide occurring in 2025 was used to validate the nowcasting results. The results indicated that the landslides were ranked within the forecasted hazard distribution, with percentile values of 87%, 90%, 93%, and 99%, respectively, denoting the occurrence of landslides within the top 13%–1% of the most hazardous slope units at the time of prediction. One event lay in the extreme hazard class (>99th percentile), highlighting the model’s strong discriminatory capability. Finally, the forecast results for each day were updated in a Google Earth Engine application to aid policymakers and planners in developing better mitigation and preparedness strategies. This study represents the first of its kind landslide nowcasting system in Mandi district using the information obtained from landslide susceptibility and rainfall-derived triggering parameters, thus offering meaningful insight into a practical decision-support tool for policymakers and disaster management authorities.