Evaluation of Artificial Neural Networks (ANNs) and Multivariate Adaptive Regression Splines (MARS) for Monthly Mean Land Surface Temperature (LST) Modelling– A Case Study of Aowin District, The Republic of Ghana
DOI:
https://doi.org/10.58825/jog.2025.19.1.185Keywords:
Artificial Intelligence, Back Propagation Neural Network, Multivariate Adaptive Regression Splines, Data Mining, Land Surface Temperature (LST)Abstract
Accurate and precise estimations of Land Surface Temperature (LST) are essential in climatology, agribusiness, agronomy, urban planning, aviation, and hydrology studies. In this study, the feasibility of two soft computing methods, thus; fifteen different Artificial Neural Network (ANN) architectures and the data mining model of Multivariate Adaptive Regression Splines (MARS) is evaluated for predicting the monthly mean LST of Aowin District, Ghana. Various weather prediction variables, including precipitation, relative humidity, wind speed, and temperature time series historical data spanning 37 years (from 1st January 1985 to 31st December 2022), were used. The data was obtained from a satellite database repository and used in the ANN and MARS models' formulation as input (independent variables) and output (dependent variable), respectively. Five different statistical performance indicators, namely mean error (ME), root mean absolute error (RMAE), mean squared error (MSE), root mean squared error (RMSE), and standard deviation (SD), were used to assess the accuracy and precision of LST estimates from both the ANN and MARS models for the research area. The results demonstrate the capability of both techniques in predicting the monthly mean LST. However, the MARS model produced the best LST estimate, with statistical metrics of ME, RMAE, MSE, RMSE, and SD being 1.8705E-07 °C, 0.0004 °C, 3.3449 °C, 5.7835 °C, and 1.6000E-09 °C, respectively. Both ANN and MARS methods can be effectively applied for LST estimation in the research region and for studying the potential impacts of climate change dynamics globally.
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