Enhancing Temperature and Rainfall Prediction Accuracy Through Deep Learning Frameworks
DOI:
https://doi.org/10.70715/jitcai.2025.v2.i2.018Keywords:
Artificial Intelligence, Deep Learning, Temperature, Rainfall, Climate ForecastingAbstract
The information of accurate forecasting on temperature, rainfall is also very crucial for disaster preparedness, as well for climate management. Typical statistical and machine learning approaches have limited ability to capture nonlinear and spatiotemporally varying structure of climate fields. This research utilized recent state-of-the-art deep learning models to improve the prediction models for both temperature and rainfall. The hybrid Convolutional Neural Networks and Long Short-Term Memory (CNN–LSTM) method achieved the best results (R² = 0.98 for temperature, 0.91 for rainfall), outperforming those of Multiple Linear Regression (MLR) and Random Forest (RF) as a traditional model. The Physics-Informed Neural Network (PINN) model delivered physically consistent and stable predictions, especially under extreme weather such as heavy rainfall or heatwaves. Relative humidity, atmospheric pressure and sea surface temperature were found as most important predictors-base on feature importance analysis. The regional analysis remained that the coastal region performed best, whereas the hilly region with the high topographical complexity presented a relatively lower accuracy. In general, embedding deep learning into physical constraints ended up improving a lot both correctness and robustness of predictions. Further work should be carried out to improve interpretability, inclusiveness of data and transferability in space of such models with the ambition to build a more sustainable real-time weather forecasting system.
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Copyright (c) 2025 Mohammad Sohel Kabir, Abdullah Al Naseeh Chowdhury, Mozibur Rahman, Md. Rasel, Md Muhasin Ali (Author)
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