Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_51,
        author={Abdullahi Muhammad and Xiaodong Li and Jun Feng},
        title={Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={ANN ARIMA Forecasting Water demand},
        doi={10.1007/978-3-030-32388-2_51}
    }
    
  • Abdullahi Muhammad
    Xiaodong Li
    Jun Feng
    Year: 2019
    Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_51
Abdullahi Muhammad1,*, Xiaodong Li1,*, Jun Feng1,*
  • 1: Hohai University
*Contact email: uwaisabdullahi87@yahoo.com, xiaodong.li@hhu.edu.cn, fengjun@hhu.edu.cn

Abstract

In various research fields such as medicine, science, marketing, engineering and military. Artificial intelligence approaches have been applied, mainly due to their powerful reasoning capability, flexibility, modeling and forecasting capacity. In this paper, an attempt to review urban water demand forecasting using various artificial intelligence based approaches such as fuzzy logic systems, support vector machines, extreme learning machines, ANN and an ARIMA as well as hybrid models which consist of an integration of two or more artificial intelligence approaches are applied. The paper illustrates how the different artificial intelligence approaches plays a vital role in urban water demand forecasting while recommending some future research directions.