Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach

Tong，Xuneng1; You，Luhua2; Zhang，Jingjie2,3,4; He，Yiliang5; Gin，Karina Yew Hoong1,2

2022-05-15

10.1016/j.jhazmat.2022.128492

JOURNAL OF HAZARDOUS MATERIALS   影响因子和分区

0304-3894

1873-3336

Monitoring and predicting the occurrence and dynamic distributions of emerging contaminants (ECs) in the aquatic environment has always been a great challenge. This study aims to explore the potential of fully utilizing the advantages of combining traditional process-based models (PBMs) and data-driven models (DDMs) with general water quality indicators in terms of improving the accuracy and efficiency of predicting ECs in aquatic ecosystems. Two representative ECs, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), in a tropical reservoir were chosen for this study. A total of 36 DDMs based on different input datasets using Artificial Neural Networks (ANN) and Random Forests (RF) were examined in three case studies. The models were applied in prognosis validation based on easily accessible data on water quality indicators. Our results revealed that all the models yielded good fits when compared to the observed data. These new insights into the advantages using the combination of traditional PBMs and DDMs with general water quality datasets help to overcome the constraints in terms of model accuracy and efficiency as well as technical and budget limitations due to monitoring surveys and laboratory experiments in the study of fate and transport of ECs in aquatic environments.

Data-driven model Emerging contaminants Hybrid model Process-based model Water quality indicators

SCI ; EI

英语

通讯

WOS:000762503900006

ENGINEERING

2-s2.0-85124818706

Scopus

1.Department of Civil & Environmental Engineering,National University of Singapore,Singapore,1 Engineering Drive 2,117576,Singapore
2.E2S2-CREATE,NUS Environmental Research Institute,National University of Singapore,Singapore,1 Create way, Create Tower, #15–02,138602,Singapore
3.Shenzhen Municipal Engineering Lab of Environmental IoT Technologies,Southern University of Science and Technology,Shenzhen,518055,China
4.Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun,130102,China
5.School of Environmental Science and Engineering,Shanghai Jiao Tong University,Shanghai,200240,China

Tong，Xuneng,You，Luhua,Zhang，Jingjie,et al. Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach[J]. JOURNAL OF HAZARDOUS MATERIALS,2022,430.

Tong，Xuneng,You，Luhua,Zhang，Jingjie,He，Yiliang,&Gin，Karina Yew Hoong.(2022).Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach.JOURNAL OF HAZARDOUS MATERIALS,430.

Tong，Xuneng,et al."Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach".JOURNAL OF HAZARDOUS MATERIALS 430(2022).