Modeling and Estimation of Reference Evapotranspiration using Machine Learning Algorithms: A Comparative Performance Analysis

Satendra Kumar Jain^* and Anil Kumar Gupta

Department of Computer Science and Applications, Barkatullah University, Bhopal, M.P., India.

Corresponding Author E-mail:satendra.k.jain@gmail.com

DOI : http://dx.doi.org/10.12944/CARJ.12.1.35

Abstract:

Fresh, clean water is necessary for human health. Currently, the agriculture sector uses the majority of freshwater for irrigation without using planning or optimization techniques. Evapotranspiration, which may have a major impact in planning water supply management and crop yield improvement, is an element of the hydrological cycle. Accurate anticipation of reference evapotranspiration (ET_O) is an intricate job due to its nonlinear behavior. Machine learning approach based model may be an intelligent tool to predict the accurate ET_O. This study investigates and compares the predictive skills of three regression based supervised learning algorithms: decision tree (dtr), and random forest (rfr), and k-nearest-neighbors (knnr) along with tuning their hyper-parameters like how many neighbors there are in knnr, minimum samples in dtr at a leaf node and quantity of trees in the rfr scenario to forecast ET_O. Every model's performance is quantified on four different groups of meteorological parameters. Groups are created based on close correlation of meteorological parameters with ETo. In this investigation, analysis is carried out on daily meteorological information of New Delhi, India for the periods from 2000 to 2021. The predicted results of the knnr, dtr and rfr models on four groups of meteorological inputs (twelve different models) are compared with ET_O obtained from the FAO-PM56 equations. The study's conclusions show that the k-nearest-neighbors and random forest regression-based models outperform the decision tree regression models concerning performance. The finest performance noted by knnr and rfr models with r2 (coefficient of determination) of 0.99 and rmse of 0.21 and 0.22 mm/day respectively whereas dtr model noted r2 of 0.98 and rmse of 0.40 mm/day.  Therefor these models may provide scientists, engineers, and farmers with more potent choices for managing water resources and scheduling irrigation.

Keywords:

Decision tree regression; FAO-PM56;  Hyper-parameter tuning; K-nearest-neighbors regression; Random forest regression

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