Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods - Gammad, Alyanna Marrielle Cabang (Animo Repository, 2023)
Temperature is a key variable for understanding climate variability and change. However, climatological summary statistics based on daily minimum and maximum temperatures contain inhomogeneities that can largely affect estimations done at a local scale. Thus, this study aimed to use a spatiotemporal Gaussian process model (STGPM) to impute and predict three-hourly measurements from neighboring stations in the Greater Manila Area that may be used to obtain summaries of temperature extremes. Due to the computationally expensive nature of this method, temperature extrema predictions using Artificial Neural Network (ANN) methods such as feed-forward back propagation (FFBP), radial basis function network (RBFN), and generalized regression neural network (GRNN) have been obtained as an alternative and for prediction performance comparison. Here, comparisons are made using R2, MSE, RMSE, and IA. As the ANN models are limited to the prediction of temperature extrema, the STGPM inherently gives more information through the imputation and prediction of three-hourly temperature. When comparing performance scores, The STGPM also performs best based on the measures of model performance namely MSE, RMSE, R2, and IA. However, among the ANN models, GRNN performs best in terms of the following measures of model performance: R2, MSE, and RMSE.