Artificial neural network based water quality index (WQI) for river Godavari (India)

Nayak, Jyotiprakash G.; Patil, L.G.; Patki, Vinayak

doi:10.1016/j.matpr.2021.03.100

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…A regular monitoring system analyzes 13 water quality variables in three layers monthly or weekly, while the automatic monitoring system analyzes eight surface water quality variables daily. [23] compared the Levenberg Marquardt (LM) algorithm and the Scaled Conjugate Gradient (SCG) algorithm to develop WQI based on the ANN approach (i.e., ANNWQI). It was observed that the LM algorithm outperforms the SCG algorithm for prediction of the ANNWQI of Indian streams, while the Bayesian Regularization algorithm was not found to be suitable for the same purpose in the present study.…”

Section: Discussionmentioning

confidence: 99%

“…To exploit the capabilities of ANNs, special hardware devices are being developed [22]. The ANN approach offers various advantages to problem-solving such as [23]: (i) previous knowledge of the undertaken study is not required in a neural network application; (ii) complex relationships between different parameters of the undertaken study are not required to be ascertained; (iii) for the development of an ANN model, assumption of constraints is not required; and (iv) an ANN model can always reach an optimal solution condition, whereas an optimization model can give the solution only once it has been completely processed. These attributes of ANN models make them an appropriate tool for providing solutions to different problems of hydrological modeling [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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“…As a result, by gathering samples and gathering data at particular locations, the physical, chemical, and biological parameters-also known as variables-can be used to test the water quality of any given body of water [6]. One tool for assessing water quality is the Water Quality Index (WQI), which is also one of the best ways to summarize the quality of the water since it condenses a lot of information into a single number, making it easier to understand and present the facts in an understandable way [7]. To better understand water quality, several researchers have conducted a variety of studies [8,9].…”

Section: Introductionmentioning

confidence: 99%

Application of Water Quality Index in Assessing water Quality: A Case Study of the Shatt Al Arab River.

Hadi Hamza

2024

Technium

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This investigation pertains to the evaluation of water quality in the Shatt Al-Arab River, located in the Basrah province of southern Iraq, from 2017 to 2022. Its quality should be assessed regularly, and the condition of water resources should be maintained accordingly. The most common analytical method for describing and assessing general water quality is the Water Quality Index (WQI). Various physicochemical parameters like pH, total dissolved solids, total hardness, calcium, magnesium, chloride, and total alkalinity are considered for the present study. The Weighted Arithmetic Water Quality Index Technique is used to evaluate these data. For checking the portability of the parameters within the acceptable limit, WHO is adopted. According to the study mentioned above, the quality has been found to be very low compared to the WQI value. It is also observed that the water was found unsuitable for drinking purposes, and it is considered harmful to human health; therefore, it requires some kind of processing before use. This study will be beneficial to policymakers for identifying and providing details about water quality in the form of a specific value.

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“…By evaluating the health impacts of toxic metal exposure to a reference dose (RfD) and cancer slope factors (CSF), the hazard quotient (HQ) is applied to define the carcinogenic and non-carcinogenic health impacts of toxic metals [12,13]. Many scientists used the WQI and health hazard identification to gauge the purity of the water [5,10,[14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Appraisal of Heavy Metal Content in the Groundwater at W-W El Minya District of Egypt

Asmoay,

Mousa,

Mohamed

2023

Egypt. J. Chem.

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In W-W El Minya district, 33 groundwater samples were collected, analyzed, and rated using various indices to assess water quality. The samples exhibited weakly acidic to faintly alkaline pH levels, excessive mineralization, weak to moderate salinity, and high hardness. The prevailing ions in the samples were Cl, Na, HCO3, and SO4 due to silicate weathering and hydrolysis of evaporite minerals. However, the World Health Organization's (WHO) recommended limits for Ca, Na, and Cl contents were exceeded in some samples. Piper's graph showed that all samples were of the SO4. Na-Cl type. Among heavy metals, Cd was the most prevalent, with 36% of samples exceeding the WHO safe level and 18% exceeding Food and Agriculture Organization (FAO) permissible values. The Heavy Metal Pollution Index (HPI) indicated that 48% of samples were suitable for drinking, while the Heavy Metal Evaluation Index (HEI) showed negligible levels of heavy metal pollution. The health risk assessment revealed non-carcinogenic and carcinogenic effects of Cd in the contaminated water. The Corrosion Ratio (CR) index suggested that metallic pipes could contaminate water with heavy metals over time. The Chloride Mass Balance (CMB) approach estimated that groundwater supply in the area was only 20.6% of total precipitation. To prevent adverse effects on residents' health, it is recommended that contaminated water be treated for cadmium pollutants.

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Artificial neural network based water quality index (WQI) for river Godavari (India)

Cited by 21 publications

References 27 publications

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Application of Water Quality Index in Assessing water Quality: A Case Study of the Shatt Al Arab River.

Appraisal of Heavy Metal Content in the Groundwater at W-W El Minya District of Egypt

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