A CNN–LSTM Deep Learning Framework for Multivariate Temperature Forecasting and Extreme Weather Event Analysis

Abstract

Throughout history weather predication has been a powerful and necessary tool in a number of activities that have had a major influence on human development and survival such as climate monitoring, agricultural planning and disaster management. Unlike other variables, temperature fluctuations are a major challenge for prediction due to the fact that they are nonlinear and extremely dynamic. This research paper introduces deep learning (DL) architectures for multivariate temperature forecasting using past weather data of single cities in India as samples. The DL methods such-as LSTM, GRU, Hybrid CNN-LSTM and Attention-based model were first outlined and then experimented. The meteorological variables that were considered include humidity, precipitation, wind speed and cloud cover. The quality of forecasting is quantified by the means of MAE, RMSE, MAPE and R2. Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model. Out of the deep learning models which were experimented with the CNN-LSTM model gave the best performance and has the following results: MAE (287.37), RMSE (401.60), MAPE (7.35%) and R2 (0.893). Besides that, CNN-LSTM model not only performed better in normal conditions but also in extreme temperatures and was able to achieve R2 of 0.923, which underscores its capacities to capture complex weather changes. This paper provides strong evidence of the value of combining DL methods for weather prediction. In the future, efforts will be made to enhance the accuracy of prediction by using transformer-based time-series models and incorporating larger spatiotemporal climate data.