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Information Journal Paper

Title

AN ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF PRESSURE FILTERS PERFORMANCE AND DETERMINATION OF OPTIMUM TURBIDITY FOR COLI-FORM AND TOTAL BACTERIA REMOVAL

Pages

  129-136

Abstract

 In water treatment processes, because of complicated and nonlinear relationships between a number of physical, chemical and operational parameters, using analytical models with the ability to capture underlying relationships using examples of the desired input-output mapping is quite suitable. ARTIFICIAL NEURAL NETWORKS (ANN) has been increasingly applied in the area of environmental and water resources engineering. The main advantage of ARTIFICIAL NEURAL NETWORKS over physical-based models is that they are data-driven.

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  • Cite

    APA: Copy

    BADALIANS GHOLIKANDI, G., HAZRATI, H., & ROSTAMIAN, H.. (2012). AN ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF PRESSURE FILTERS PERFORMANCE AND DETERMINATION OF OPTIMUM TURBIDITY FOR COLI-FORM AND TOTAL BACTERIA REMOVAL. JOURNAL OF ENVIRONMENTAL STUDIES, 37(60), 129-136. SID. https://sid.ir/paper/3258/en

    Vancouver: Copy

    BADALIANS GHOLIKANDI G., HAZRATI H., ROSTAMIAN H.. AN ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF PRESSURE FILTERS PERFORMANCE AND DETERMINATION OF OPTIMUM TURBIDITY FOR COLI-FORM AND TOTAL BACTERIA REMOVAL. JOURNAL OF ENVIRONMENTAL STUDIES[Internet]. 2012;37(60):129-136. Available from: https://sid.ir/paper/3258/en

    IEEE: Copy

    G. BADALIANS GHOLIKANDI, H. HAZRATI, and H. ROSTAMIAN, “AN ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF PRESSURE FILTERS PERFORMANCE AND DETERMINATION OF OPTIMUM TURBIDITY FOR COLI-FORM AND TOTAL BACTERIA REMOVAL,” JOURNAL OF ENVIRONMENTAL STUDIES, vol. 37, no. 60, pp. 129–136, 2012, [Online]. Available: https://sid.ir/paper/3258/en

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