Explainable Deep Learning Models for Long-Term Temperature and Rainfall Forecasting
Abstract
Climate impact assessment, agricultural planning, and disaster preparedness requires accurate long-term prediction of temperature and rainfall. Conventional statistical models are frequently not able to represent nonlinear and long-term relationships in climate data, and deep learning models, however powerful in prediction, cannot be interpreted. The given paper suggests an interpretable deep learning architecture, which uses Long Short-Term Memory (LSTM) networks and SHAP-based explainability to forecast multivariate climate. Sliding window sequences are used to process historical data (temperature, rainfall, humidity and atmospheric pressure) to obtain seasonal and long-term trends. The proposed model predicts both temperature and rainfall simultaneously, and it has a better performance than the ARIMA, LSTM, GRU and CNN-LSTM models in the terms of RMSE, MAE and Accuracy. Moreover, the explainability module offers insights into feature and time importance, which increases the level of transparency and trust in the prediction. Empirical evidence shows that the described method is effective in balancing predictive accuracy of 92% and understandability, which can be used in climate decision support systems.