Modelling reference evapotranspiration using principal component analysis and machine learning methods under different climatic environments

Author:

Raza Ali1,Saber Kouadri2,Hu Yongguang1,L. Ray Ram3,Ziya Kaya Yunus4,Dehghanisanij Hossein5,Kisi Ozgur67,Elbeltagi Ahmed8

Affiliation:

1. School of Agricultural Engineering Jiangsu University Zhenjiang China

2. Laboratory of Water and Environment Engineering in Saharan Environment University of Kasdi Merbah‐Ouargla Ouargla Algeria

3. Department of Agriculture, Nutrition and Human Ecology, College of Agriculture and Human Sciences Prairie View A&M University Prairie View Texas USA

4. Civil Engineering Department, Faculty of Engineering Osmaniye Korkut Ata University Osmaniye Turkey

5. Agricultural Research, Education and Extension Organization Agricultural Engineering Research Institute Karaj Alborz Iran

6. Department of Civil Engineering Technical University of Lübeck Lübeck Germany

7. Department of Civil Engineering Ilia State University Tbilisi Georgia

8. Agricultural Engineering Department, Faculty of Agriculture Mansoura University Mansoura Egypt

Abstract

AbstractReference evapotranspiration (ETo) is a complex process in the hydrologic cycle that influences several hydrologic parameters. Although several methods have been developed to model ETo, a reliable method that can use limited climatic input parameters for data‐limited regions is still limited. This study evaluated four machine learning (ML) methods: M5 pruned (M5P) tree, sequential minimal optimization (SMO), radial basis function neural regression (RBFNreg) and multilinear regression (MLR). The major objective of this study was to identify the best approach to estimate ETo with minimum input data in five stations (Multan, Jacobabad, Faisalabad, Islamabad and Skardu) located in Pakistan. The datasets of these stations comprised maximum and minimum temperatures (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n) variables. Two scenarios were used for ETo modelling. In the first scenario, five climatic variables were used as inputs to estimate ETo as obtaining full climatic parameters is the biggest challenge in developing countries. Principal component analysis (PCA) was used as a clustering technique in the second scenario to reduce the climatic input parameters. The PCA results indicated that Tmax, Tmin and n were identified as effective inputs for ETo estimation. Based on statistical indicators, the M5P tree outperformed the other applied ML methods in estimating ETo under various climatic environments. This study recommends focusing on areas with high ETo values and adequate irrigation scheduling of crops to achieve water sustainability.

Funder

Government of Jiangsu Province

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Wiley

Subject

Soil Science,Agronomy and Crop Science

Reference42 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3