EARLY DETECTION OF NATURAL DISASTER: FOCUS ON FLOODS IN INDIA
DOI:
https://doi.org/10.63503/c.acset.2025.15Keywords:
Flood detection, early warning system, remote sensing, hydrological modeling, IoT, machine learning, India, disaster risk reductionAbstract
Floods represent one of the most severe and recurring natural disasters in India, accounting for substantial human, economic, and environmental losses every year. With nearly 40 million hectares of land prone to flooding, India is among the most flood-affected countries in the world. The dense populations along major river basins such as the Ganges, Brahmaputra, and Godavari make communities highly vulnerable to inundation, property damage, agricultural disruption, and long-term displacement. Early detection and timely warning are therefore critical in reducing loss of life and minimizing the socioeconomic burden caused by flood events. [7], [12]
Need for Early Detection
Traditional flood forecasting methods, such as rainfall-runoff models and statistical trend analysis, provide valuable insights but often suffer from limitations in accuracy and lead time. These methods struggle to capture nonlinear interactions between rainfall, river discharge, soil saturation, and topography. Moreover, delays in disseminating alerts mean that even when forecasts are generated, affected communities may not have enough time to act. [1], [10], [12]
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