Deep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI

2025-11-14

AI Summary

To address the limited interpretability of deep learning models for precipitation forecasting—which hinders operational deployment—this paper proposes an interpretable short-term (1–5 day) deep learning framework tailored to four major climatic zones in India (Bengaluru, Mumbai, Delhi, Kolkata). Methodologically, we design a city-adaptive Time-Distributed CNN-ConvLSTM architecture integrated with ERA5 reanalysis data and incorporate multimodal eXplainable AI (xAI) techniques: Grad-CAM for spatiotemporal saliency mapping, permutation importance for identifying dominant meteorological variables, and temporal occlusion with counterfactual perturbation to quantify temporal sensitivity. Experiments demonstrate high accuracy (RMSE: 0.21–1.80 mm/day) and robustness across diverse climatic regimes. This work constitutes the first systematic implementation of a regionally adaptive, end-to-end interpretable precipitation forecasting pipeline, significantly enhancing transparency and trustworthiness of AI-driven meteorological decision-making.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops interpretable deep learning for short-term precipitation prediction in Indian cities
• Addresses black-box limitations in weather forecasting using ConvLSTM architecture
• Provides transparent insights into precipitation patterns across diverse climate zones

Innovation

• Hybrid Time-Distributed CNN-ConvLSTM architecture for prediction
• City-specific model optimization with varying convolutional filters
• Explainable AI techniques for transparent precipitation forecasting