ESTIMATION OF HYDRAULIC CONDUCTIVITY BY USING OF SCMAI MODEL MARAGHE-BONAB AQUIFER AS A CASE STUDY

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YUSEFZADEH S.; NADIRI A.A.

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Journal: GEOSCIENCES Year:2017 | Volume:27 | Issue:105 Page(s): 183-192

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Title

ESTIMATION OF HYDRAULIC CONDUCTIVITY BY USING OF SCMAI MODEL MARAGHE-BONAB AQUIFER AS A CASE STUDY

Author(s)

YUSEFZADEH S. | NADIRI A.A. | Issue Writer Certificate

Keywords

WAVELET-NEURAL NETWORKQ1

SUPPORT VECTOR MACHINEQ1

FUZZY LOGICQ2

HYDRAULIC CONDUCTIVITYQ1

SUPERVISED COMPOUND ARTIFICIAL INTELLIGENCEQ1

Abstract

Nowadays ground water is the main source of drinking, agriculture and other uses in arid and semi-arid areas. The demand for this critical and strategic natural resource has increased with population growth and society developments. This process decreases water resources and damages aquifers environment. Therefore, we need to manage aquifers and understand the hydrogeological parameters to deal with water crisis and prevent distraction of the aquifers. HYDRAULIC CONDUCTIVITY is one of the most important parameters for aquifer management. The ground water system is complex and estimation of hydrogeological parameters is associated with inherent uncertainty and also is costly and time consuming that is usually done with classical methods such as laboratory tests, slug test, tracing test and pumping tests. Therefore recently estimation of hydroulic conductivity by artificial intelligence methods has reduced the uncertainty of this parameter and add up some accuracy, so that it can overcome the shortcoming of classical methods. In this study, four artificial intelligence methods including Mamdani FUZZY LOGIC (MFL) system, Sugeno FUZZY LOGIC (SFL) system, WAVELET-NEURAL NETWORK method and Least square SUPPORT VECTOR MACHINE (LS-SVM) method were used as individual models to estimate the HYDRAULIC CONDUCTIVITY using surface geophysical data in Maragheh-Bonab aquifer. Given that each of these models based on their inherent properties presented good results in some parts of area, therefore, for parallel use of the four models the nonlinear combination method as a supervised committee machine artificial intelligence (SCMAI) model was used to estimate the HYDRAULIC CONDUCTIVITY in the Maragheh-Bonab aquifer. The result of this model showed that this new combinational model has higher performance than other single models presented by using different evaluation criteria. The SCMAI model was tested against 15 data. The RMSE and R2 for SCMAI prediction were computed as 0.045 and 0.97, respectively. A comparison between the values estimated by this model with those by individual mentioned models indicated that SCMAI model performs better than individual AI models supported by lower RMSE and higher R2. This result implies that SCMAI model shows high performance for estimatiing the HYDRAULIC CONDUCTIVITY values in the heterogeneous unconfined aquifer in the Maragheh-Boanb plain.

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APA: Copy

YUSEFZADEH, S., & NADIRI, A.A.. (2017). ESTIMATION OF HYDRAULIC CONDUCTIVITY BY USING OF SCMAI MODEL MARAGHE-BONAB AQUIFER AS A CASE STUDY. GEOSCIENCES, 27(105 ), 183-192. SID. https://sid.ir/paper/32262/en

Vancouver: Copy

YUSEFZADEH S., NADIRI A.A.. ESTIMATION OF HYDRAULIC CONDUCTIVITY BY USING OF SCMAI MODEL MARAGHE-BONAB AQUIFER AS A CASE STUDY. GEOSCIENCES[Internet]. 2017;27(105 ):183-192. Available from: https://sid.ir/paper/32262/en

IEEE: Copy

S. YUSEFZADEH, and A.A. NADIRI, “ESTIMATION OF HYDRAULIC CONDUCTIVITY BY USING OF SCMAI MODEL MARAGHE-BONAB AQUIFER AS A CASE STUDY,” GEOSCIENCES, vol. 27, no. 105 , pp. 183–192, 2017, [Online]. Available: https://sid.ir/paper/32262/en

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