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Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann

Author

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  • S. Vijay

    (Vivekanandha College of Arts and Sciences for Women (Autonomous))

  • K. Kamaraj

    (SSM College of Arts Science)

Abstract

Determination of the drastic changes in water quality is an urgent need in this polluted era and is more essential for the survival of the existing and growing water demand. It has been very difficult to analyze the water quality data. This study focused on the Water Quality Index (WQI) prediction of water samples collected from 1944 different wells surrounding the Vellore district. WQI prediction is carried out by ANN (i.e.) Artificial Neural Networks implementation which has used 15 groundwater variables that are collected in different parts of the Vellore district from 2008 to 2017. If 15 underground variable values meet the desired range then WQI is considered as better and appropriate for drinking. But if any one of the value doesn’t meet the desired range then it is not considered as better and hence not suitable for drinking. In this study the pre-processing of the collected data has been completed to reduce the computational time. Further feature extraction techniques are used to extract the required features. The extracted features are passed on to ANN classifiers that possess three activation functions like Tanh, Maxout, and rectifier. The novelty of this paper is that WQI is determined by combining the three activation functions like Tanh, Maxout, and rectifier. A comparative analysis has been performed for proposed work related with various methodologies.

Suggested Citation

  • S. Vijay & K. Kamaraj, 2021. "Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 535-553, January.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02729-8
    DOI: 10.1007/s11269-020-02729-8
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    References listed on IDEAS

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    1. Ivana I. Mladenović-Ranisavljević & Lj. Takić & Đ. Nikolić, 2018. "Water Quality Assessment Based on Combined Multi-Criteria Decision-Making Method with Index Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2261-2276, May.
    2. Hafiza Mamona Nazir & Ijaz Hussain & Mazhar Iqbal Zafar & Zulifqar Ali & Nasser M. AbdEl-Salam, 2016. "Classification of Drinking Water Quality Index and Identification of Significant Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4233-4246, September.
    3. Maryam Malekzadeh & Saeid Kardar & Keivan Saeb & Saeid Shabanlou & Lobat Taghavi, 2019. "A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1609-1628, March.
    4. Mohammad Ali Baghapour & Mohammad Reza Shooshtarian & Mahdi Zarghami, 2020. "Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1155-1172, February.
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    Cited by:

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    3. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.
    4. Parvin Golfam & Parisa-Sadat Ashofteh, 2022. "Performance Indexes Analysis of the Reservoir-Hydropower Plant System Affected by Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5127-5162, October.
    5. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    6. Monika Kulisz & Justyna Kujawska & Bartosz Przysucha & Wojciech Cel, 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network," Energies, MDPI, vol. 14(18), pages 1-17, September.
    7. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    8. Laís Régis Salvino & Heber Pimentel Gomes & Saulo de Tarso Marques Bezerra, 2022. "Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2779-2793, June.

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