Ghosh, Tanmay and Shaurabh, Anand and Nannewar, Rakesh Gomaji and Nagaraj, Nithin (2025) Deep learning for short‑term precipitation prediction in four major Indian cities: A ConvLSTM approach with explainable AI. arXiv.
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2511.11152 - Published Version Download (47kB) |
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| Abstract: | Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretable deep learning framework for short-term precipitation prediction in four major Indian cities: Bengaluru, Mumbai, Delhi, and Kolkata, spanning diverse climate zones. We implemented a hybrid Time-Distributed CNN-ConvLSTM (Convolutional Neural Network-Long Short-Term Memory) architecture, trained on multi-decadal ERA5 reanalysis data. The architecture was optimized for each city with a different number of convolutional filters: Bengaluru (32), Mumbai and Delhi (64), and Kolkata (128). The models achieved root mean square error (RMSE) values of 0.21 mm/day (Bengaluru), 0.52 mm/day (Mumbai), 0.48 mm/day (Delhi), and 1.80 mm/day (Kolkata). Through interpretability analysis using permutation importance, Gradient-weighted Class Activation Mapping (Grad-CAM), temporal occlusion, and counterfactual perturbation, we identified distinct patterns in the model's behavior. The model relied on city-specific variables, with prediction horizons ranging from one day for Bengaluru to five days for Kolkata. This study demonstrates how explainable AI (xAI) can provide accurate forecasts and transparent insights into precipitation patterns in diverse urban environments. |
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| Item Type: | Journal Paper |
| Subjects: | School of Natural and Engineering Sciences School of Natural and Engineering Sciences > Complex Systems |
| Divisions: | Schools > Natural Sciences and Engineering |
| Date Deposited: | 27 Apr 2026 12:34 |
| Last Modified: | 27 Apr 2026 12:34 |
| Official URL: | https://arxiv.org/abs/2511.11152 |
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| Funders: | * |
| Projects: | * |
| DOI: | https://doi.org/10.48550/arXiv.2511.11152 |
| URI: | http://eprints.nias.res.in/id/eprint/3321 |
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