IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i2d10.1007_s11269-020-02729-8.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-020-02729-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-020-02729-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chao Liu & Mingshuang Xu & Yufeng Liu & Xuefei Li & Zonglin Pang & Sheng Miao, 2022. "Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study," IJERPH, MDPI, vol. 19(23), pages 1-14, November.
    2. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zoran Štirbanović & Vojka Gardić & Dragiša Stanujkić & Radmila Marković & Jovica Sokolović & Zoran Stevanović, 2021. "Comparative MCDM Analysis for AMD Treatment Method Selection," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3737-3753, September.
    2. Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.
    3. Yongxiang Zhang & Ruitao Jia & Jin Wu & Huaqing Wang & Zhuoran Luo, 2021. "Evaluation of Groundwater Using an Integrated Approach of Entropy Weight and Stochastic Simulation: A Case Study in East Region of Beijing," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    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.
    5. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Georgios K. Koulinas & Alexandros S. Xanthopoulos & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "Risks Ranking in a Desalination Plant Construction Project with a Hybrid AHP, Risk Matrix, and Simulation-Based Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3221-3233, August.
    7. 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.
    8. Akram Seifi & Mohammad Ehteram & Vijay P. Singh & Amir Mosavi, 2020. "Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN," Sustainability, MDPI, vol. 12(10), pages 1-42, May.
    9. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    10. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02729-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.