On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction

By A. Ghaemi, Mohammad Rezaie-Balf, Jan Adamowski, Ozgur Kisi, John M. Quilty. Agricultural and Forest Meteorology, 2019

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Abstract Accurate pan evaporation ( E p a n ) prediction is a critical issue in water resources management, particularly when designing and managing rural water resource systems, and when assessing water utilization and demand. In this study, Multivariate Adaptive Regression Spline (MARS) and M5 Model Tree (MT) models were coupled with a maximum overlap discrete wavelet transform (MODWT) to create M A R S M O D W T and M T M O D W T models for the prediction of monthly pan evaporation for Turkey’s Siirt and Diyarbakir meteorological stations. The performance of the standalone MARS and MT models was compared to the corresponding MODWT-based hybrid models. Furthermore, the developed hybrid models were combined with ( E p a n ) Mallow’s coefficient ( C p ) to minimize the number of predictor variables needed to predict monthly E p a n . The models used preprocessed input data, including temperature ( T ), wind speed ( W ), relative humidity ( RH ), and solar radiation ( SR ). The performance of each approach was evaluated using standard statistical measures ( i.e ., correlation coefficient ( R ), root mean square error ( RMSE ), Nash-Sutcliffe efficiency ( NSE ) and mean absolute error ( MAE )). The results showed that the M A R S C p M O D W T model improved the MARS accuracy with respect to lower percentages of RMSE (29.46%) and MAE (24.53%) in the validation phase for the Siirt station. In case of the Diyarbakir station, the M A R S C p M O D W T improvements decreased the RMSE (17.91%) and MAE (16.49%) values in comparison to standalone MARS model. The overall results indicated that the use of both MODWT and C p as pre-processing techniques improves prediction accuracy, and thus, they are both recommended for use in further studies.

Subfields: Atmospheric Science, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/fc646ab2627f899b16f451019014fee04a14865a