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Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Author

Listed:
  • Zaher Mundher Yaseen

    (Ton Duc Thang University)

  • Majeed Mattar Ramal

    (University of Anbar)

  • Lamine Diop

    (Université Gaston Berger (UGB) BP 234-Saint Louis
    The Ohio State University)

  • Othman Jaafar

    (National University of Malaysia)

  • Vahdettin Demir

    (Karatay University)

  • Ozgur Kisi

    (Ilia State University)

Abstract

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Suggested Citation

  • Zaher Mundher Yaseen & Majeed Mattar Ramal & Lamine Diop & Othman Jaafar & Vahdettin Demir & Ozgur Kisi, 2018. "Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2227-2245, May.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:7:d:10.1007_s11269-018-1915-7
    DOI: 10.1007/s11269-018-1915-7
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    References listed on IDEAS

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    1. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    2. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
    3. Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.
    4. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
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    Cited by:

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    2. Mingjing Guo & Ziyu Jiang & Yan Bu & Jinhua Cheng, 2019. "Supporting Sustainable Development of Water Resources: A Social Welfare Maximization Game Model," IJERPH, MDPI, vol. 16(16), pages 1-15, August.
    3. Hye-Suk Yi & Sangyoung Park & Kwang-Guk An & Keun-Chang Kwak, 2018. "Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea," IJERPH, MDPI, vol. 15(10), pages 1-20, September.
    4. Qingqing Zhang & Xue-yi You, 2024. "Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 235-250, January.
    5. Lamine Diop & Saeed Samadianfard & Ansoumana Bodian & Zaher Mundher Yaseen & Mohammad Ali Ghorbani & Hana Salimi, 2020. "Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 733-746, January.

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