A Comparison between MLR, MARS, SVR and RF Techniques: Hydrological Time-series Modeling

Abstract

Pan evaporation modeling is an essential part of water resources management and water budget governance. The study's objective was to examine the suitability of regression and tree-based techniques for estimating pan evaporation from climatic variables. Multiple linear regression (MLR), multivariate adaptive regression splines (MARS), support vector machine (SVM) and random forest (RF) techniques are employed for weekly pan evaporation modeling for the Ranichauri station situated in the Mid-Himalayan region of Uttarakhand, India. The determination of the most appropriate inputs among climatic variables to map evaporation was done by regression approaches. The data was divided into two parts: the first three years of data used for calibration and the remainder of the one-year data used for model validation. Statistical indices such as root mean square error (RMSE), Nash Sutcliffe coefficient of efficiency (NSE), and coefficient of determination (R2) were used to assess the performance of weekly pan evaporation estimating models. Based on scatter plots, the results are under-predicted and over-predicted for the weekly pan evaporation values. The results showed that the values of the RMSE values ranged from 0.542 to 0.689, the NSE values ranged from 0.953 to 0.974, and the highest R2value was found for the SVR model for the testing period. Therefore, the SVR model was found to be superior and can be applied to predict weekly pan evaporation values for the Ranichauri site. Doi: 10.28991/HEF-2022-03-01-07 Full Text: PDF

Keywords

• pan evaporation
• mlr
• mars
• svm
• random forest
• hydrology.