Browse > Article
http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1053

Optimum conditions for artificial neural networks to simulate indicator bacteria concentrations for river system  

Bae, Hun Kyun (Department of Global Environment, School of Environment, Keimyung University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1053-1060 More about this Journal
Abstract
Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.
Keywords
Artificial neural networks; Water quality monitoring; Total coliform; Water resource management;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hsu, K.-L. Gupta, H.V., Gao, X., Sorooshian, S., and Imam, B. (2002). "Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis". Water Resources Research. Vol. 38, No. 12, pp. 1-17.
2 Oftelie, S., Saltzstein, A., Gianos, P., Boyum, K., Rocke, R., and Mosallem, A. (2000). Infrastructure: Latest survey finds orange county voters broadly similar to national survey respondents on the priority of cleaning up coastal waters. Technical Report, The Orange County Business Council, CA, U.S., p. 73.
3 Xu, T., Coco, G., and Neale, M. (2020). "A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning." Water Research, Elsevier, Vol. 177, No. 6, pp. 115788-115799.   DOI
4 Boudaghpour, S., Moghadam, H.S.A., Hajbabaie, M., Toliati, S.H. (2019). "Estimating chlorophyll-A concentration in the Caspian Sea from MODIS images using artificial neural networks." Environmental Engineering Research, KSEE, Vol. 25, No. 4, pp. 515-521.   DOI
5 U.S. Envrionment Protection Agency (U.S. EPA) (2021). U.S., accessed 7 September 2021, .
6 Zhang, J., Qiu, H., Li, X., Niu, J., Nevers, M.B., Hu, X., and Phanikumar, M.S. (2018). "real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach." Environment Science and Technology, ACS Publications, Vol. 52, No. 15, pp. 8446-8455.   DOI
7 State Water Resources Control Board, California Environmental Protection Agency (SWRCB) (2001). Source investigations of storm drain discharges causing exceedances of bacteriological standards. U.S., p. 17.
8 Bae, H.K. (2007). Modeling approaches to predict conditions of water quality using physical, chemical, and hydrological data focused on biological contaminations. Ph.D. dissertation, University of California, Irvine, CA, U.S., p.102.
9 Boehm, A.B., Grant, S.B., Kim, J.H., Mowbray, S.L., McGee, C.D., Clark, C.D., Foley, D.M., and Wellman, D.E. (2002). "Decadal and shorter period variability of surf zone water quality at Huntington Beach, California." Environment Science and Technology, ACS Publications, Vol. 36, No. 18, pp. 3885-3892.   DOI
10 French, C.B. (2003). Modeling Nitrogen transport in the Newport Bay/San Diego Creek watershed. Master Thesis, University of California Riverside, RIversidem, CA, U.S., pp. 24-25.
11 Strauss, A. (2002). Total maximum daily loads for toxic pollutants San Diego Creek and Newport Bay, California, U.S. Environmental Protection Agency Region 9, Washington DC, U.S., pp. 37
12 Kamer, K., Schiff, K., Kennison, R.L., and Fong P. (2002). Macro-algal nutrient dynamics in upper Newport Bay. Technical Report, Southern California Coastal Water Research Project, CA, U.S., p. 33.
13 Natural Resources Defense Council (NRDC) (2021). U.S., accessed 15 September 2021, .
14 Reeves, R.L., Grant, S.B., Mrse, R.D., Copil-Oancea, C.M., Sanders, B.F., and Boehm, A.B. (2004). "Scaling and management of fecal indicator bacteria in runoff from a Coastal Urban Watershed in Southern California." Environment Science and Technology, ACS Publications, Vol. 38, No. 9, pp. 2637-2648.   DOI
15 Surfrider Foundation (2021). U.S., accessed 22 September 2021, .
16 Vijayashanthar, V., Qiao, J., Zhu, Q., Entwistle, P., and Yu, G. (2018). "Modeling fecal indicator bacteria in urban waterways using artificial neural networks." Journal of Environmental Engineering, ASCE, Vol. 144, No. 6, doi: 10.1061/(ASCE)EE.1943-7870.0001377.   DOI
17 Searcy, R.T., and Boehm, A.B. (2021). "A Day at the beach: Enabling Coastal water quality prediction with high-frequency sampling and data-driven models." Environment Science and Technology, ACS Publications, Vol. 55, No. 3, pp. 1908-1918.   DOI
18 U.S. Fish & Wildlife Service (USFW) (2021). U.S., accessed 13 September 2021, .
19 Corrigan, J.A., Butkus, S.R., Ferris, M.E., and Roberts, J.C. (2021). "Microbial source tracking approach to investigate fecal waste at the Strawberry Creek watershed and Clam Beach, California, USA." International Journal of Environmental Research and Public Health, Vol. 18, No. 13, p. 6901.   DOI