干旱区生态环境知识资源中心(Arid): Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

Arid

DOI	10.1109/ACCESS.2021.3100490
	Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
	Al-Sulttani, Ali Omran; Al-Mukhtar, Mustafa; Roomi, Ali B.; Farooque, Aitazaz Ahsan; Khedher, Khaled Mohamed; Yaseen, Zaher Mundher
通讯作者	Yaseen, ZM (corresponding author), Al Ayen Univ, Sci Res Ctr, New Era & Dev Civil Engn Res Grp, Thi Qar 64001, Iraq. ; Yaseen, ZM (corresponding author), Asia Univ, Coll Creat Design, Taichung, Taiwan.
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2021
卷号	9 页码:108527-108541
英文摘要	An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R-2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R-2, RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.
英文关键词	Predictive models Rivers Water resources Water quality Water pollution Computational modeling Adaptation models Semi-arid region river water quality biochemical oxygen demand principal component analysis
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000683985800001
WOS关键词	WRAPPER FEATURE-SELECTION ; SUPPORT VECTOR MACHINES ; RANDOM FOREST ; GENETIC ALGORITHMS ; NEURAL-NETWORKS ; CLASSIFICATION ; REGRESSION ; DESIGN
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363549
作者单位	[Al-Sulttani, Ali Omran] Univ Baghdad, Coll Engn, Dept Water Resources Engn, Baghdad 17635, Iraq; [Al-Mukhtar, Mustafa] Univ Technol Baghdad, Civil Engn Dept, Baghdad 19006, Iraq; [Roomi, Ali B.] Minist Educ, Directorate Educ Thi Qar, Thi Qar 64001, Iraq; [Roomi, Ali B.] Al Ayen Univ, Sci Res Ctr, Biochem & Biol Engn Res Grp, Thi Qar 64001, Iraq; [Farooque, Aitazaz Ahsan] Univ Prince Edward Isl, Fac Sustainable Design Engn, Charlottetown, PE C1A 4P3, Canada; [Khedher, Khaled Mohamed] King Khalid Univ, Coll Engn, Dept Civil Engn, Abha 61421, Saudi Arabia; [Khedher, Khaled Mohamed] Mrezgua Univ Campus, High Inst Technol Studies, Dept Civil Engn, Nabeul 8000, Tunisia; [Yaseen, Zaher Mundher] Al Ayen Univ, Sci Res Ctr, New Era & Dev Civil Engn Res Grp, Thi Qar 64001, Iraq; [Yaseen, Zaher Mundher] Asia Univ, Coll Creat Design, Taichung, Taiwan
推荐引用方式 GB/T 7714	Al-Sulttani, Ali Omran,Al-Mukhtar, Mustafa,Roomi, Ali B.,et al. Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction[J],2021,9:108527-108541.
APA	Al-Sulttani, Ali Omran,Al-Mukhtar, Mustafa,Roomi, Ali B.,Farooque, Aitazaz Ahsan,Khedher, Khaled Mohamed,&Yaseen, Zaher Mundher.(2021).Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction.IEEE ACCESS,9,108527-108541.
MLA	Al-Sulttani, Ali Omran,et al."Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction".IEEE ACCESS 9(2021):108527-108541.