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Prediction of irrigation water quality indices based on machine learning and regression models

文献类型：期刊论文

作者Mokhtar, Ali1,2; Elbeltagi, Ahmed3; Gyasi-Agyei, Yeboah4; Al-Ansari, Nadhir5; Abdel-Fattah, Mohamed K.6
刊名APPLIED WATER SCIENCE
出版日期2022-04-01
卷号12期号:4页码:14
关键词Irrigation water quality index Machine learning Support vector machine Stepwise regression Bahr El-Baqr drain
ISSN号2190-5487
DOI10.1007/s13201-022-01590-x
通讯作者Al-Ansari, Nadhir(nadhir.alansari@ltu.se) ; Abdel-Fattah, Mohamed K.(mohammedkama18@yahoo.com)
英文摘要Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly's ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3-) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
WOS关键词SUPPORT VECTOR MACHINE ; GLOBAL SOLAR-RADIATION ; GROUNDWATER QUALITY ; STATISTICAL-ANALYSIS ; INFILTRATION ; VARIABLES
WOS研究方向Water Resources
语种英语
WOS记录号WOS:000771530900001
出版者SPRINGER HEIDELBERG
源URL[http://ir.igsnrr.ac.cn/handle/311030/172843]  
专题中国科学院地理科学与资源研究所
通讯作者Al-Ansari, Nadhir; Abdel-Fattah, Mohamed K.
作者单位1.Northwest Agr & Forestry Univ, Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
2.Cairo Univ, Fac Agr, Dept Agr Engn, Giza 12613, Egypt
3.Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt
4.Griffith Univ, Sch Engn & Built Environm, Nathan, Qld 4111, Australia
5.Lulea Univ Technol, Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden
6.Zagazig Univ, Fac Agr, Soil Sci Dept, Zagazig 44511, Egypt
推荐引用方式
GB/T 7714		 Mokhtar, Ali,Elbeltagi, Ahmed,Gyasi-Agyei, Yeboah,et al. Prediction of irrigation water quality indices based on machine learning and regression models[J]. APPLIED WATER SCIENCE,2022,12(4):14.
APA Mokhtar, Ali,Elbeltagi, Ahmed,Gyasi-Agyei, Yeboah,Al-Ansari, Nadhir,&Abdel-Fattah, Mohamed K..(2022).Prediction of irrigation water quality indices based on machine learning and regression models.APPLIED WATER SCIENCE,12(4),14.
MLA Mokhtar, Ali,et al."Prediction of irrigation water quality indices based on machine learning and regression models".APPLIED WATER SCIENCE 12.4(2022):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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