Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning

Over the last decade, the combination of both big data and machine learning research area's receiving considerable attention and expedite the prospect of the agricultural industry. This research aims to gain insights into a state-of-the-art big data application in smart farming. An essential issue for agriculture planning is to estimate evapotranspiration accurately because it plays a pivotal role in irrigation water scheduling for using water efficiently. This article presents H2O model framework to determine the daily ET0 for Hoshiarpur and Patiala districts of Punjab. The effects of four supervised learning algorithms: Deep Learning-Multilayer Perceptrons (DL), Generalized Linear Model (GLM), Random Forest (RF), and Gradient-Boosting Machine (GBM) and also evaluate the overall ability to predict future ET0. Analysis of these four models, perform in H2O framework. This framework presents a new criterion to train, validate, test and improve the classification efficiency using machine learning algorithms. The performance of the DL model is compared with other state-of-art of models such as RF, GLM and GBM. In this respect, our analysis depicts that models presents high performance for modeling daily ET0, (e.g. NSE = 0.95-0.98, r(2) = 0.95-0.99, ACC = 85-95, MSE = 0.0369-0.1215, RMSE = 0.1921-0.2691).