Neural Network-Based Modeling of Water Quality in Jodhpur, India
In this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/9/5/92 |
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| author | Krishna Kumar Sinha Mukesh Kumar Gupta Malay Kumar Banerjee Gowhar Meraj Suraj Kumar Singh Shruti Kanga Majid Farooq Pankaj Kumar Netrananda Sahu |
| author_facet | Krishna Kumar Sinha Mukesh Kumar Gupta Malay Kumar Banerjee Gowhar Meraj Suraj Kumar Singh Shruti Kanga Majid Farooq Pankaj Kumar Netrananda Sahu |
| author_sort | Krishna Kumar Sinha |
| collection | DOAJ |
| description | In this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using American Public Health Association (APHA) specified methodology, eight WQ parameters, viz., pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium hardness (Ca-H), residual chlorine, nitrate (as NO<sub>3</sub><sup>−</sup>), and chloride (Cl<sup>−</sup>), were selected for describing the water quality for potability use. The quality of each parameter is examined as a function of the zone. Taking the average parametric values of different zones, a unique number was used to describe the overall quality of water. It was found that the average value of each parameter varies significantly with zones. Further, we used neural network (NN) modeling to map the nonlinear relationship between the above eight parametric inputs and the water quality index as the output. It can be observed that the NN designed in the present work acquired sufficient learning and can be satisfactorily used to predict the relational pattern between the input and the output. It can further be observed that the water quality index (WQI) from this work is highly efficient for a successful assessment of water quality in the study area. The major challenge to uniquely describing the drinking water quality lies in understanding the cumulative effect of various parameters affecting the quality of water; the quantified figure is subjected to debate, and this paper addresses the difficulty through a novel approach. The framework presented in this work can be automated with appropriate equipment and shall help government agencies understand changing water quality for better management. |
| first_indexed | 2024-03-10T03:46:49Z |
| format | Article |
| id | doaj.art-da8dd7eef6ab45778b46a06b4b911880 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-10T03:46:49Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-da8dd7eef6ab45778b46a06b4b9118802023-11-23T11:18:11ZengMDPI AGHydrology2306-53382022-05-01959210.3390/hydrology9050092Neural Network-Based Modeling of Water Quality in Jodhpur, IndiaKrishna Kumar Sinha0Mukesh Kumar Gupta1Malay Kumar Banerjee2Gowhar Meraj3Suraj Kumar Singh4Shruti Kanga5Majid Farooq6Pankaj Kumar7Netrananda Sahu8Department of Energy and Electrical Engineering, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, IndiaDepartment of Energy and Electrical Engineering, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, IndiaDepartment of Energy and Electrical Engineering, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, IndiaGovernment of Jammu & Kashmir, Department of Ecology, Environment & Remote Sensing, SDA Colony Bemina, Bemina 190018, Srinagar, IndiaCentre for Sustainable Development, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, IndiaCentre for Climate Change, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, IndiaGovernment of Jammu & Kashmir, Department of Ecology, Environment & Remote Sensing, SDA Colony Bemina, Bemina 190018, Srinagar, IndiaInstitute for Global Environmental Strategies, Hayama 240-0115, Kanagawa, JapanDepartment of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, Delhi, IndiaIn this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using American Public Health Association (APHA) specified methodology, eight WQ parameters, viz., pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium hardness (Ca-H), residual chlorine, nitrate (as NO<sub>3</sub><sup>−</sup>), and chloride (Cl<sup>−</sup>), were selected for describing the water quality for potability use. The quality of each parameter is examined as a function of the zone. Taking the average parametric values of different zones, a unique number was used to describe the overall quality of water. It was found that the average value of each parameter varies significantly with zones. Further, we used neural network (NN) modeling to map the nonlinear relationship between the above eight parametric inputs and the water quality index as the output. It can be observed that the NN designed in the present work acquired sufficient learning and can be satisfactorily used to predict the relational pattern between the input and the output. It can further be observed that the water quality index (WQI) from this work is highly efficient for a successful assessment of water quality in the study area. The major challenge to uniquely describing the drinking water quality lies in understanding the cumulative effect of various parameters affecting the quality of water; the quantified figure is subjected to debate, and this paper addresses the difficulty through a novel approach. The framework presented in this work can be automated with appropriate equipment and shall help government agencies understand changing water quality for better management.https://www.mdpi.com/2306-5338/9/5/92water quality parametersBIS standardswater quality indexneural network |
| spellingShingle | Krishna Kumar Sinha Mukesh Kumar Gupta Malay Kumar Banerjee Gowhar Meraj Suraj Kumar Singh Shruti Kanga Majid Farooq Pankaj Kumar Netrananda Sahu Neural Network-Based Modeling of Water Quality in Jodhpur, India Hydrology water quality parameters BIS standards water quality index neural network |
| title | Neural Network-Based Modeling of Water Quality in Jodhpur, India |
| title_full | Neural Network-Based Modeling of Water Quality in Jodhpur, India |
| title_fullStr | Neural Network-Based Modeling of Water Quality in Jodhpur, India |
| title_full_unstemmed | Neural Network-Based Modeling of Water Quality in Jodhpur, India |
| title_short | Neural Network-Based Modeling of Water Quality in Jodhpur, India |
| title_sort | neural network based modeling of water quality in jodhpur india |
| topic | water quality parameters BIS standards water quality index neural network |
| url | https://www.mdpi.com/2306-5338/9/5/92 |
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