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Water Quality Predictive Analytics Using an Artificial Neural Network with a Graphical User Interface
Open accessAbstract
Since clean water is well known as one of the crucial sources that all living things need in their daily lives, the demand for clean freshwater nowadays has increased. However, water quality is slowly deteriorating due to anthropogenic and natural sources of pollution and contamination. Therefore, this study aims to develop artificial neural network (ANN) models to predict six different water quality parameters in the Langat River, Malaysia. Moreover, an application (app) equipped with a graphical user interface (GUI) was designed and developed to conduct real-time prediction of the water quality parameters by using real-time data as inputs together with the ANN models. As for the results, all of the ANN models achieved high coefficients of determination (R2), which were between 0.9906 and 0.9998, as well as between 0.8797 and 0.9972 for training and testing datasets, respectively. The developed app successfully predicted the outcome based on the run models. The implementation of a GUI-based app in this study enables a simpler and more trouble-free workflow in predicting water quality parameters. By eliminating sophisticated programming subroutines, the prediction process becomes accessible to more people, especially on-site operators and trainees.
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References 36 · Cited by 30
Research works printed but not linked: 1
- Rizal, 2020, River water quality prediction using artificial intelligence approach: Literature review, J. Energy Environ., 12, 1
Metadata
- Authors
- Rizal, N. N. M., Hayder, G., & Yusof, K. A.
- Affiliations
- Universiti Tenaga Nasional (MY)
- Universiti Putra Malaysia (MY)
- Corresponding author
- Hayder, Gasim
- Venue
- Water
- Publisher
- MDPI AG
- Published
- 2022-04-11
- Volume
- 14
- Issue
- 8
- Pages
- 1221
- ISSN
- 2073-4441 (ISSN-L)
- OpenAlex
- W4224102652
- Cited by
- 30
- References
- 36
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 5 · 2023: 5 · 2022: 5
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Water (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Higher Education (Fundamental Research Grant Scheme (FRGS))
- Universiti Tenaga Nasional (UNITEN) (BOLD Publication Fund 2022)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality and Pollution Assessment
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- water quality prediction, artificial neural networks, graphical user interface, mobile application, real-time prediction, Langat River, water quality parameters, river pollution, water quality monitoring
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- Rizal, N. N. M., Hayder, G., & Yusof, K. A. (2022). Water Quality Predictive Analytics Using an Artificial Neural Network with a Graphical User Interface. Water. https://doi.org/10.3390/w14081221
