Applications of Soft Computing Methods in Environmental Engineering

Living reference work entry


Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In environmental engineering, researchers and engineers have successfully employed different methods of soft computing for modeling of various real-life environmental problems. In this study, applications of core soft computing techniques, such as artificial neural networks (ANN), fuzzy logic (FL), adaptive neuro fuzzy inference systems (ANFIS), and support vector machines (SVM), are investigated and important mathematical aspects of these methods are highlighted. Considering the concepts and methods, this study briefly reviews applications of soft computing techniques in the field of environmental engineering, especially in water/wastewater treatment and air quality/pollution control/forecasting. A brief introduction to complexity of environmental problems and the general definition soft computing concept are presented in the first section of this chapter. The second section comprises four subsections and presents mathematical background of four different soft computing methods. Section “Implementation of Soft Computing Methods in Environmental Engineering”, which is consisted of eight subsections, reviews successful applications of soft computing-based prediction models implemented in the field of environmental engineering and summarizes the important findings obtained in these studies. At the end of the overview of the published works on soft computing applications in different environmental areas, in the last section, some special illustrative soft computing examples and the respective MATLAB®-based solutions are presented for environmental engineers.


Adaptive neuro fuzzy inference systems (ANFIS) Aggregation Air quality/pollution control/forecasting Algorithm Artificial neural networks (ANN) Backpropagation Bayesian regulation Broyden–Fletcher–Goldfarb–Shanno (BFGS) Centroid Classification Defuzzification Early stopping Environmental engineering Feed-forward Firing strength Fletcher–Reeves Fuzzification Fuzzy inference system (FIS) Fuzzy logic (FL) Fuzzy operator Gradient descent Hessian matrix Implication Jacobian matrix Kernel functions Lagrange multipliers Levenberg–Marquardt Linguistic Logarithmic sigmoid MATLAB® Membership function Modeling Momentum factor Normalized layer (N) Polak–Ribiére Powell-Beale Prediction Product layer (π) Quasi–Newton Scaled conjugate gradient Soft computing Support vector machines (SVM) Tangent sigmoid Training Water and wastewater treatment 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Environmental Engineering, Faculty of Civil EngineeringYildiz Technical UniversityIstanbulTurkey

Section editors and affiliations

  • Chaudhery Mustansar Hussain
    • 1
  1. 1.Department of Chemistry and Environmental SciencesNew Jersey Institute of TechnologyNewarkUSA

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