Deep learning and tree-based models for earth skin temperature forecasting in Malaysian environments

Abstract

Predicting the Earth Skin Temperature (TS) using artificial intelligence (AI) has the potential to offer valuable insights into environmental changes and their impacts. TS has significant nonlinearity due to several meteorological parameters, including average temperature, maximum temperature, minimum temperature, relative humidity, surface pressure, wind speed, and wind direction. This study introduced four machine learning (ML) models, namely solo Long Short-Term Memory (LSTM), Autoencoder-LSTM, Classification and Regression Tree (CART), and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM), to predict daily averaged TS at different locations over the Malaysian region. In the first stage of the modeling development, Pearson Correlation (PC) was adopted to measure the strength and direction of the relationship between input and output variables. The statistical analysis and visual interpretations demonstrated that the Autoencoder-LSTM model outperformed the CNN-LSTM, LSTM, and CART models for each simulated city. The Autoencoder-LSTM model showed outstanding performance at Kuantan in comparison with the seven cities, achieving a coefficient of determination (R2 = 0.965), Root Mean Square Error (RMSE = 0.183), Mean Absolute Error (MAE = 0.142), and Nash-Sutcliffe Efficiency (NSE = 0.96). The research findings suggested that the coordinates of each station are crucial in determining the level of data randomness, which ultimately affects the learning process of the ML models.