| Title |
Data-Driven Modeling of Freshwater Aquatic Systems: Status and Prospects |
| Authors |
차윤경 ( Yoonkyung Chaa ) ; 신지훈 ( Jihoon Shinb ) ; 김영우 ( Youngwoo Kimc ) |
| DOI |
https://doi.org/10.15681/KSWE.2020.36.6.611 |
| Keywords |
Data-driven approach; Ecological model; Freshwater aquatic system; Water quality model; Water quality and resources management |
| Abstract |
Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects. |