Using CNN-LSTM Model for Weather Forecasting
Michael K.H. Fan, Omar Imran, Arka Singh, Samuel A. Ajila
Abstract
An efficient and cost-effective weather forecasting approach can be used to protect humans and benefit economic growth as a result of secure forest, agriculture, and tourism industry sectors. This paper is based on the IEEE Big Data IARAI’s Weather4cast 2021 challenge dataset. The goal of this paper is to consider computational cost of predicting future weather forecast by using a CNN-LSTM based neural network model. The network utilizes an encoder-decoder architecture to predict future weather images. All the four variables are predicted using the same model providing generalization in the solution. The model is trained and tested on the Nile Region (R1) data and a significant improvement is observed for the loss against cloud mask and rainfall feature prediction in comparison with CNNGRU deep learning model. Two models – shallow and deep models are compared and the results in terms of MSE values for the shallow model (which is computationally cost effective) is not too far from the deep model.