Evaluation of machine learning models for prediction of daily reference evapotranspiration in semi-arid India Singh Amit K.1,*, Singh J.B.1, Das Bappa2, Singh Ramesh3, Ghosh Avijit1, Kantwa S.R.1 1ICAR-Indian Grassland and Fodder Research Institute, Jhansi-284 003, India 2ICAR-Central Coastal Agricultural Research Institute, Ela, Old Goa-403402 3ICRISAT Development Centre, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502 324, India *Corresponding author e-mail: amit09bhu@gmail.com
Online published on 20 September, 2023. Abstract Reference evapotranspiration (ET0) is controlled by climatic factors; hence, its estimation provides an idea about the atmospheric demand of water. Machine learning techniques like elastic net (ELNET), K-nearest neighbours (KNN), multivariate adaptive regression splines (MARS), partial least squares regression (PSLR), random forest (RF), support vector regression (SVR), XGBoost and cubist were employed to predict daily reference evapotranspiration based on daily weather parameters of twenty years. Penman-Monteith method was used as the reference method for ET0 estimation. All models performed well during calibration showing higher coefficient of determination (R2) which ranged from 0.97 (for PLSR) to 1 (for cubist models). Mean absolute error during calibration ranges from 0.027 mm-1 d-1 for cubist to 0.607 mm-1 d-1 for ELNET. Cubist model (R2= 1, MAE = 0.017 mm d-1, RMSE = 0.027 mm d-1) outper formed other models during the calibration. During validation, the coefficient of determination (R2) for the machine learning models varied from 0.819 to 1, RMSE varied from 0.06 to 0.60 mm d-1 and MAE varied from 0.031 to 0.38 mm d-1. Based on statistical parameters, best performance was observed for cubist model (R2 = 1, RMSE = 0.06 mm d-1, MAE = 0.031 mm d-1) among the studied machine learning models for the prediction of reference evapotranspiration. Hence, the cubist model may be used to estimate daily reference evapotranspiration for the studied region. Top Keywords Machine learning models, Prediction, Reference evapotranspiration. Top |