Bihar's Kosi curse: How AI can predict flood risks, mitigate losses
The research focused on the Kosi Megafan, a large flood-prone area formed by centuries of sediment build-up. The team utilised satellite imagery and elevation data to analyse 21 environmental factors influencing flood behaviour.These variables included rainfall patterns, land use, drainage density, soil type, and terrain slope. By integrating this rich dataset, the researchers built a sophisticated machine-learning ensemble model that was optimised using Bayesian techniques to enhance prediction accuracy.AI MODEL TO BRING ANSWERS TO THE FLOODINGOne key advancement in this study was the incorporation of Explainable AI (XAI) methods. Unlike traditional AI models, which often operate as opaque 'black boxes,' the XAI approach enables the system to provide clear explanations behind its flood risk predictions.This transparency is critical for building trust among policymakers and local authorities, enabling them to understand why certain areas are flagged as susceptible to flooding. The resulting flood susceptibility maps offer valuable insights that can guide planning efforts, emergency response, and flood mitigation in the Kosi basin.Published in the journal Environmental and Sustainability Indicators (Impact Factor 5.6), this research was led by Md. Gufran Alam under the guidance of Prof. Mohit Prakash Mohanty, with contributions from PhD scholar Vaibhav Tripathi and ISRO scientist Dr. C.M. Bhatt.
Graphic: India Today
RESEARCH TO IMPACT MILLIONSThis research plays a vital role in enhancing the safety and resilience of millions living in flood-affected regions. In Bihar, India, seasonal floods have long caused widespread displacement, crop damage, and disruption to local livelihoods.Addressing these challenges, an AI-based flood susceptibility framework has emerged as a critical tool for proactive disaster management. Unlike conventional models, this framework not only predicts flood-prone areas but also uncovers the underlying causes, enabling local administrators, planners, and disaster response teams to take informed, timely actions.These interventions range from improved infrastructure planning to effective warning dissemination and efficient relief management, collectively steering communities toward greater climate resilience. The framework's application to the Kosi Megafan, which is a vast alluvial fan in Bihar, has highlighted varying flood susceptibility across the landscape.advertisementThe area was classified into five categories: very low (26.03%), low (19.85%), moderate (10.64%), high (8.29%), and very high (35.18%). Notably, the highest flood susceptibility was concentrated along the main Kosi River and its paleochannels, pinpointing critical zones for focused mitigation efforts.POSSIBLY A LANDMARK STUDY TO IMPROVE LIVESWhat sets this methodology apart is its scalability and adaptability. Utilising open-source data, scalable algorithms, and emphasising model transparency, the framework transcends regional boundaries. It offers a practical alternative to traditional hydrodynamic models, especially in data-scarce environments where boundary conditions and detailed historical discharge records are often unavailable.This makes it an invaluable resource not only for other flood-prone areas across India but also for similar vulnerable regions globally.By integrating cutting-edge technology with practical disaster management needs, this AI-based framework significantly contributes to achieving sustainable development goals and advancing climate adaptation strategies.Ultimately, it marks an important step toward building safer, more resilient communities in the face of increasing climate challenges.- EndsMust Watch
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