COMPARING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS IN ESTIMATING WATER QUALITY PARAMETERS IN PERIODS OF LOW AND HIGH WATER FLOW

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MONTASERI M.; ZAMAN ZAD GHAVIDEL S.

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Journal: Journal of Water and Soil Year:2017 | Volume:30 | Issue:6 Page(s): 1733-1747

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Title

COMPARING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS IN ESTIMATING WATER QUALITY PARAMETERS IN PERIODS OF LOW AND HIGH WATER FLOW

Author(s)

MONTASERI M. | ZAMAN ZAD GHAVIDEL S. | Issue Writer Certificate

Keywords

ADAPTIVE NEURO FUZZY INFERENCE SYSTEMQ2

ARTIFICIAL NEURAL NETWORKQ1

DISSOLVED SOLIDSQ1

ZARRINEH RIVERQ2

Abstract

Introduction: A total dissolved solid (TDS) is an important indicator for water quality assesment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationships of mineral salts composition with TDS.Materials and Methods: In this study, methods of ARTIFICIAL NEURAL NETWORKs with five different training algorithm, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), Fletcher Conjugate Gradient (CGF), One Step Secant (OSS) and Gradient descent with adaptive learning rate backpropagation (GDA) algorithm and adaptive Neurofuzzy inference system based on Subtractive Clustering were used to model water quality properties of ZARRINEH RIVER Basin, to be developed in total DISSOLVED SOLIDS prediction. ANN and ANFIS program code were written in MATLAB language. Here, the ANN with one hidden layer was used and the hidden nodes' number was determined using trial and error. Different activation functions (logarithm sigmoid, tangent sigmoid and linear) were tried for the hidden and output nodes. Therefore, water quality data from seven hydrometer stations were used during the statistical period of 18years (1993-2010). In this research, the study period was divided into two periods of dry and wet flow, and then in a preliminary statistical analysis, the main parameters affecting the estimation of the TDS are determined and is used for modeling. 75% of data are used for remaining and 25% of the data are used for evaluation of the model, randomly. In this paper, three statistical evaluation criteria, correlation coefficient (R), the root mean square error (RMSE) and mean absolute error (MAE) were used to assess models' performances.

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APA: Copy

MONTASERI, M., & ZAMAN ZAD GHAVIDEL, S.. (2017). COMPARING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS IN ESTIMATING WATER QUALITY PARAMETERS IN PERIODS OF LOW AND HIGH WATER FLOW. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 30(6 ), 1733-1747. SID. https://sid.ir/paper/141817/en

Vancouver: Copy

MONTASERI M., ZAMAN ZAD GHAVIDEL S.. COMPARING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS IN ESTIMATING WATER QUALITY PARAMETERS IN PERIODS OF LOW AND HIGH WATER FLOW. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2017;30(6 ):1733-1747. Available from: https://sid.ir/paper/141817/en

IEEE: Copy

M. MONTASERI, and S. ZAMAN ZAD GHAVIDEL, “COMPARING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS IN ESTIMATING WATER QUALITY PARAMETERS IN PERIODS OF LOW AND HIGH WATER FLOW,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 30, no. 6 , pp. 1733–1747, 2017, [Online]. Available: https://sid.ir/paper/141817/en

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