Smart River, India: A Multimodal AI Framework for Streamflow Forecasting and Hydrological Risk Prediction

Abstract

River flow and the hydrology system always create positive information on how the water system should be handled. The process here supports the control facilities of flood and health plans to maintain their protection for local habitat. In countries like India, the system here is more critical based on the weather, which poses a great deal of river-based issues for the people nearby.
In the Indian subcontinent, rain does not always happen in every area on the places and particular Times. Amazing part of the process Southeast monsoon session which creates the words like Ganga as well as Godavari major floods at least once a time in a year.
There is some other hydrology models based on physical rules create the particular nature of processing the complex behaviour of large river systems. The models need to be carefully tuned to maintain the detailed data-based setting in many developing areas, which in a normal way not normally accessible.
To address these all challenges, this paper here presents 'Smart River, India', a novel multimodal artificial intelligence framework to maintain designed for continuous streamflow-based forecasting as well as real-time hydrological risk classification herein. The proposed architecture always maintains concurrently processes 2D spatial remote sensing data via Convolutional Neural Networks (CNNs), 1D temporal hydro-meteorological sequences which here using Long Short-Term Memory (LSTM) or Transformer networks, as well as static catchment attributes through Multi-Layer Perceptrons (MLPs). Evaluated based nature of such aspects across the contrasting terrains of the Brahmaputra & Godavari river basins, this particular multimodal fusion effectively captures extreme monsoon responses here. The framework here shown outperforms traditional & unimodal deep learning baselines, achieving more of a peak Nash-Sutcliffe Efficiency (NSE) of 0.94 as well as providing highly reliable flood risk categorization to support regional disaster management.