Titlebook: Computational Intelligence for Water and Environmental Sciences; Omid Bozorg-Haddad,Babak Zolghadr-Asli Book 2022 The Editor(s) (if applic

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Die vernetzte Konsumgüterbrancheent and statistical models, includes Radial Based Function Neural Network (RBFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Feedforward Neural Network (FFNN), Linear Regression (LR), Generalized Linear Regression (GLR), and Support Vector Regression (SVR). An example of a real urban water dist

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Optimization Algorithms Surpassing Metaphorhe number of algorithms based on natural behaviours has increased, the majority of them deal with some ordeals such as being stuck in local optimal results. Hence, these ordeals pave the way for the advent of new mathematical, population-based techniques. In this chapter, three powerful metaphor-fre

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A Survey of PSO Contributions to Water and Environmental Sciencesvey revealed that PSO has been employed both solely and collaboratively with other approaches like machine learning techniques and simulation software to solve single- and multiple-objective problems of different sectors from surface water to renewable energy generation.

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Data Mining Methods for Modeling in Water Scienceff 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, a

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Deep Learning Application in Water and Environmental Sciencesn appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies.

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Support Vector Machine Applications in Water and Environmental Sciencesls concept and its application in water and environmental sciences. Furthermore, this chapter introduces different types of SVM models and other emerging ones. Finally, the challenges of this method for future studies will be discussed.