Modeling water quality parameters using data-driven models, a case study Abu-Ziriq Marsh in South of Iraq
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Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values of these parameters cause low water quality indices. In addition, they play a significant role in hydrous life, effective water resources management and health studies. Thus, it is of critical importance to identify the optimum modeling method that would be capable to capture the behavior of these parameters. The aim of this study was to assess the ability of using three different models of artificial intelligence techniques: Adaptive neural based fuzzy inference system (ANFIS), artificial neural networks (ANNs) and Multiple Regression Model (MLR) to predict and estimate TDS and EC in Abu-Ziriq marsh south of Iraq. As so, eighty four monthly TDS and EC values collected from 2009 to 2018 were used in the evaluation. The collected data was randomly split into 75% for training and 25% for testing. The most effective input parameters to model TDS and EC were determined based on cross-correlation test. The three performance criteria: correlation coefficient (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of the developed models. It was found that nitrate (NO3), calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most influential inputs on TDS. While calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most effective on EC. The comparison of the results showed that the three models can satisfactorily estimate the total dissolved solids and electrical conductivity, but ANFIS model outperformed the ANN and MLR models in the three performance criteria: RMSE, CC and NSE during the calibration and validation periods in modeling the two water quality parameters. ANFIS is recommended to be used as a predictive model for TDS and EC in the Iraqi marshes.
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Some studies have adopted ML techniques and DSS such as ANN, DT, GA, FL, and ANFIS to analyze water chemistry and assess water quality.
“Some studies have adopted ML techniques and DSS, such as ANN, DT, GA, FL, and ANFIS, to analyze water chemistry and assess water quality (e.g., Ref. [163,164]).”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [163]
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- Authors
- Al-Mukhtar, M., & Al-Yaseen, F.
- Affiliations
- University of Technology - Iraq (IQ)
- Corresponding author
- Al-Mukhtar, Mustafa
- Al-Yaseen, Fuaad
- Venue
- Hydrology
- Publisher
- MDPI AG
- Published
- 2019-03-17
- Volume
- 6
- Issue
- 1
- Pages
- 24
- ISSN
- 2306-5338 (ISSN-L)
- OpenAlex
- W2921197958
- Cited by
- 96
- References
- 43
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 14 · 2023: 17 · 2022: 19 · 2021: 11 · 2020: 12 · 2019: 7
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Hydrology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- 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 › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Keywords
- total dissolved solids, electrical conductivity, water quality prediction, artificial neural networks, adaptive neuro-fuzzy inference system, multiple linear regression, salinity, water quality index
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