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Data Mining Methods for Modeling in Water Science

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Computational Intelligence for Water and Environmental Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1043))

Abstract

One of the most useful research fields with many real-life applications, such as in water science, is the subject of data mining. Data mining (DM) is considered a process to extract valuable data from a wide range of information stored in various databases. The data is categorized into the form of patterns, associations, changes, anomalies and significant structures. In water recourses management and environmental engineering, predicting and modelling parameters play an integral role in decision making. The most critical freshwater water resource for millions of people worldwide are rivers with a dynamic nature (floods/droughts), in terms of available freshwater quantity and quality. With various basin characteristics, river flow and sediment regime may be influenced by natural processes such as erosion and sediment transport as well as anthropogenic factors such as urban stormwater runoff and semi-treated sanitary/industrial sewage discharge. Therefore, artificial intelligence (AI) techniques are used to decrease model development costs and improve prediction errors, achieving more efficient models. In this chapter, some well-known techniques and AI-based methods are introduced, and their applications are elaborated. The models are comprised of extreme learning machine (ELM), least square support vector machine (LSSVM), genetic programming (GP), adaptive neural-fuzzy inference system (ANFIS), and multivariate adaptive regression spline (MARS). Each technique, then, is illustrated with a brief literature review. Having being evaluated in terms of the basic concept, the methods are addressed based on a mathematical statement. In the last part, the pseudocode of the ways, an acceptable guideline for coding the methods, is pointed out. This chapter is collected for graduate students, researchers, educators, and practitioners interested in engineering optimization.

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Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyedehelham Shirvani-Hosseini

  2. Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

    Arvin Samadi-Koucheksaraee & Iman Ahmadianfar

  3. School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada

    Bahram Gharabaghi

Authors
  1. Seyedehelham Shirvani-Hosseini
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  2. Arvin Samadi-Koucheksaraee
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  3. Iman Ahmadianfar
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  4. Bahram Gharabaghi
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Corresponding author

Correspondence to Seyedehelham Shirvani-Hosseini .

Editor information

Editors and Affiliations

  1. College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran

    Omid Bozorg-Haddad

  2. QUEX Joint-researcher, Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD, Australia

    Babak Zolghadr-Asli

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Shirvani-Hosseini, S., Samadi-Koucheksaraee, A., Ahmadianfar, I., Gharabaghi, B. (2022). Data Mining Methods for Modeling in Water Science. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_8

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