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The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters
Abstract
This paper presents the use of artificial neural networks (ANNs) as a viable means of forecasting water quality parameters. A review of ANNs is given, and a case study is presented in which ANN methods are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. It is estimated that high salinity levels in the Murray cause $US 22 million damage per year to water users in Adelaide. Previous studies have shown that the average salinity of the water supplied to Adelaide could be reduced by about 10% if pumping from the Murray were to be scheduled in an optimal manner. This requires forecasts of salinity several weeks in advance. The results obtained were most promising. The average absolute percentage errors of the independent 14‐day forecasts for four different years of data varied from 5.3% to 7.0%. The average absolute percentage error obtained as part of a real‐time forecasting simulation for 1991 was 6.5%.
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Citations
Cited by
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Self-citationGevrey, M., Dimopoulos, I., & Lek, S. (2003) · Ecological Modelling · doi:10.1016/s0304-3800(02)00257-0 · Cited by 1,248
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063
Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2008) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2007.10.001 · Cited by 522
IoT Based Real-time River Water Quality Monitoring System
Chowdury, M. S. U., Emran, T. B., Ghosh, S., Pathak, A., Alam, M. M., Absar, N., Andersson, K., & Hossain, M. S. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.08.025 · Cited by 319
Metadata
- Authors
- Maier, H. R., & Dandy, G. C.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 1996-04
- Volume
- 32
- Issue
- 4
- Pages
- 1013-1022
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W2023567628
- Cited by
- 572
- References
- 0
- FWCI
- 13.81
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maier, H. R., & Dandy, G. C. (1996). The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters. Water Resources Research. https://doi.org/10.1029/96wr03529
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
