Journal of Food, Agriculture and Environment

Comparison of kriging, ANN and ANFIS models for spatial and temporal distribution modeling of groundwater contaminants

Author(s):

Ali Reza Farahmand ^1*, Mohammad Manshouri ², Abdolmajid Liaghat ³, Hossein Sedghi ²

Recieved Date: 2010-06-21, Accepted Date: 2010-10-26

Abstract:

Spatial and temporal quality distribution is an important factor in groundwater management. Due to sampling sites deficit, high cost and time limit, spatial and temporal distribution modeling of aquifer contaminants is needed. Determining the best and the most suitable model is also very essential which is the main aim in this study. Electrical conductivity (EC) and chloride (Cl^-) are two important indicators for water quality assessment. Electrical conductivity and chloride spatial and temporal distribution modeling with kriging, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods is investigated in this research. Also methods performance is evaluated. Groundwater spatial and temporal distribution of EC and Cl^- are gained from 43 observation wells in Firoozabad plain (Iran) during 1992 to 2007, which collected by Fars Regional Water Organization. Groundwater resources have an important role in this region due to surface water deficit. In kriging method, for spatial and temporal quality parameters, spherical model was the dominant type of model that fitted the data. Different Artificial Neural Network structures were examined and four neurons in hidden layer recognized as the most efficient structure. Also in ANFIS method, different types of membership function such as Gaussian, bell shape, and trapezoid for inputs of model, were used. Bell shape type and three number of membership functions in input parameters recognized as the most efficient structure. As a result, ANFIS model, compared to kriging and ANN models, has a higher efficiency in estimation of the groundwater spatial and temporal quality distribution.

Keywords:

ANFIS, Artificial Neural Network, geostatistics, groundwater quality, kriging, spatial and temporal distribution

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Journal: Journal of Food, Agriculture and Environment
Year: 2010
Volume: 8
Issue: 3&4
Category: Environment
Pages: 1146-1155

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