Utilizing Artificial Intelligence and Remote Sensing to Predict Flooding in Real-Time and Address Climate Resilience Policy in South Asia

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Year : 2026 | Volume : 17 | 01 | Page :
    By

    Yug Raman Srivastava,

  1. Student,, Rajiv Gandhi National University of Law, Punjab, India

Abstract

South Asia, a region characterized by hydro-climatic instability, faces an intensifying risk from devastating flooding, aggravated by human-induced climate change and intricate river basin interactions. Traditional flood prediction systems, based on limited in-situ data and resource-intensive physical models, have serious delays and resolution problems that make it harder to reduce disaster risk. The combined applications of Artificial Intelligence (AI) and high-resolution remote sensing (RS) constitute a paradigm shift in real-time flood prediction and pre-disaster resilience building. The research also proposes a generic framework wherein machine learning algorithms—specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks—are trained on multi-modal satellite data stream (e.g., Synthetic Aperture Radar, satellite altimetry, and precipitation estimate) to provide high-resolution, dynamic flood inundation maps with unprecedented lead times. This paper discusses the effectiveness of that framework through case studies in the Ganges- Brahmaputra-Meghna (GBM) and Indus basins, demonstrating its superior predictive power relative to traditional models. Ultimately, the research position that this technological convergence is more than an early warning system; it provides detailed, actionable information, which means there is a need to shift climate resilience policy from a reactive, post-disaster approach to a proactive adaptive approach. The discussion also addresses the issues of governance and justice that come with these kinds of technocratic approaches. It argues for a co-production model which situates this technology within local social and political contexts to increase the chance that it will work, and be used equitably.

Keywords: Artificial Intelligence, Remote Sensing, Flood Prediction, Climate Resilience, South Asia, Hydrological Modelling, Policy Integration

How to cite this article:
Yug Raman Srivastava. Utilizing Artificial Intelligence and Remote Sensing to Predict Flooding in Real-Time and Address Climate Resilience Policy in South Asia. Journal of Remote Sensing & GIS. 2026; 17(01):-.
How to cite this URL:
Yug Raman Srivastava. Utilizing Artificial Intelligence and Remote Sensing to Predict Flooding in Real-Time and Address Climate Resilience Policy in South Asia. Journal of Remote Sensing & GIS. 2026; 17(01):-. Available from: https://journals.stmjournals.com/jorsg/article=2026/view=239729


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Ahead of Print Subscription Review Article
Volume 17
01
Received 21/03/2026
Accepted 24/03/2026
Published 04/04/2026
Publication Time 14 Days
62

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