RNN-Based Weather Forecast Prediction: A Modern Approach

Abstract

Accurate weather forecasting is vital for resource planning, disaster management, and informed decision-making. This study leverages deep learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and 1D Convolutional Neural Networks (1D CNN), to predict weather trends from sequential data. Extensive preprocessing was conducted to ensure data integrity and compatibility with the models. Among the evaluated models, the 1D CNN and LSTM demonstrated superior performance, achieving regression accuracies of 83.09% and 83.44%, respectively. The GRU model also performed reliably, with a regression accuracy of 81.72%. A Hybrid CNN-LSTM model was tested but exhibited significantly lower performance, highlighting the robustness of standalone LSTM and CNN models for weather prediction tasks. Future work will focus on incorporating larger datasets, enhancing interpretability through explainable AI, and exploring more advanced architectures. This research underscores the effectiveness of deep learning models for time-series forecasting and provides valuable insights into their comparative performance.