Integrating Physics-Informed Neural Networks with Hydrological Models for Floods and Landslide Forecasting

Floods and landslides are significant natural hazards that frequently threaten the Indian Himalayas, an extremely sensitive region both geologically and climatically. Shallow slope failure due to heavy rainfall during rainstorms is common in mountain areas. Existing forecasting models are usually either purely data-driven and thus suffer from the "black-box" issue and poor generalization, or purely physics-based, which are computationally expensive and extremely challenging to calibrate. This research presents a hybrid framework combining a Physics-Informed Neural Network (PINN) for flood forecasting and an Extreme Gradient Boosting (XGBoost) model for landslide susceptibility prediction. It serves as a compromise between purely data-driven and physics-based models. Our method is an integration of observed data consisting of high priority variables - precipitation records, soil moisture data, and Digital Elevation Models (DEMs) with the underlying governing partial differential equations (PDEs). By incorporating physical equations and laws into the neural network, the Hydrological model obtains proficiency such that the results align well with observed data and physical behavior, governed by the physical laws. The proposed method is applied in a highly dynamic Himalayan region (Himachal Pradesh), a region that frequently witnesses the two natural hazards.