首页|Data on Machine Learning Published by a Researcher at Barkatullah University (Mo deling and Estimation of Reference Evapotranspiration using Machine Learning Alg orithms: A Comparative Performance Analysis)
Data on Machine Learning Published by a Researcher at Barkatullah University (Mo deling and Estimation of Reference Evapotranspiration using Machine Learning Alg orithms: A Comparative Performance Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News - Investigators publish new report on artificial in telligence. According to news reporting from Bhopal, India, by NewsRx journalist s, research stated, “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.” Our news editors obtained a quote from the research from Barkatullah University: “Evapotranspiration, which may have a major impact in planning water supply man agement and crop yield improvement, is an element of the hydrological cycle. Acc urate anticipation of reference evapotranspiration (ETO) is an intricate job due to its nonlinear behavior. Machine learning approach based model may be an inte lligent tool to predict the accurate ETO. This study investigates and compares t he predictive skills of three regression based supervised learning algorithms: d ecision tree (dtr), and random forest (rfr), and k-nearest-neighbors (knnr) alon g 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 ETO. Every model’s performance is quantified on four different group s 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 gro ups of meteorological inputs (twelve different models) are compared with ETO obt ained from the FAO-PM56 equations.”
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