Towards Data-Driven Weather Intelligence in Palestine: A Multi-Station Benchmark of Classical Machine Learning and Deep Learning Models
Precise weather forecasting plays a critical role in sectors such as agriculture, transport, energy management, and climate change adaptation, and machine learning and deep learning algorithms have been widely used for data-driven time series forecasting problems. In this work, we explore the application of machine learning and deep learning models for multi-weather variable forecasting in a dataset recorded over a period of ten years (2015–2025) for five weather stations in Palestine. The dataset comprises measurements for temperature, relative humidity, wind speed, precipitation, atmospheric pressure, and sunshine hours. To avoid the issue of temporal leakage, a chronological training, validation, and test set splitting approach was used in the evaluation experiments. The models used in this study include ARIMA, SARIMA, Random Forest, XGBoost, CNN, LSTM, GRU, ConvLSTM, CNN-GRU, and CNN-LSTM with station embeddings. Our experimental results indicate that the XGBoost model achieved the highest performance in predicting temperature and relative humidity (R2 = 0.953 and R2 = 0.670, respectively), while deep learning methods exhibited high accuracy across several weather features. The CNN-LSTM model was successfully able to learn temporal–spatial patterns via station embeddings, while recurrent neural networks performed impressively in forecasting sunshine hours and atmospheric pressure.