Modelling of Maximum Daily Water Temperature for Streams: Optimally Pruned Extreme Learning Machine (OPELM) versus Radial Basis Function Neural Networks (RBFNN)

Abstract

This study proposed two data-driven models, namely the optimally pruned extreme learning machine (OPELM) and the radial basis functions neural networks (RBFNN) to predict maximum daily water temperature in streams. Air temperature (Ta), flow discharge (Q) and the day of the year (DOY) were used as predictors. Four indicators, including the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE) were used in evaluating the performances of different models. The present study was conducted according to four different scenarios. First, the OPELM and RBFNN models were developed and validated for each station separately. For the three other scenarios, the models were developed using data from one station and validated for the two other stations separately. Modelling results showed that in the proposed models Ta and Q may not be sufficiently informative and the addition of DOY significantly contributes to better capturing the seasonal pattern of the maximum daily water temperature in streams. Generally, OPELM models outperformed RBFNN models, and overall, the modelling results indicated that the OPELM models developed in this study can be effectively used for predicting maximum water temperature in streams.