Abstract
Accurate temperature and rainfall (T&R) forecasting is vital for the climate-sensitive regions of Northern India, particularly Jammu, Kashmir, and Ladakh, where volatile weather patterns significantly affect livelihoods, socio-economic development, and disaster management efforts. Despite their importance, traditional forecasting methods often fall short due to their high computational demands and inability to provide localized, real-time predictions, leaving a critical research gap in addressing these challenges. This study addresses the need for precise and efficient T&R forecasting using deep learning-based framework tailored to the unique climatic conditions of these regions. The major research focus is to develop and evaluate a model capable of capturing complex temporal dependencies in localized time-series weather data. Utilizing data from the Indian Meteorological Department (IMD) for Jammu, Srinagar, and Ladakh stations covering the period from January 1, 2000, to December 31, 2023, the proposed framework employs recurrent neural networks (RNN) and long short-term memory (LSTM) architectures, both optimized for time-series forecasting. Key findings reveal that while both RNN and LSTM models exhibit robust performance in single input single output (SISO) setups, RNN model consistently outperforms the LSTM in capturing intricate temporal relationships. The RNN model in MIMO configuration achieved significantly lower mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) for Jammu, Srinagar, and Ladakh, with respective values of [0.0636, 0.1011, 0.0401] for Jammu, [0.1048, 0.1555, 0.0455] for Srinagar, and [0.0854, 0.1344, 0.0411] for Ladakh. These results underscore the RNN model's precision, making it a practical tool for real-time weather forecasting. By enhancing the accuracy of T&R predictions in regions with challenging meteorological conditions, this study contributes to improved climate adaptation strategies, disaster preparedness, and sustainable development. Its findings hold broader implications for advancing localized forecasting technologies in other regions with similar climatic complexities.