Abstract

This paper considers the modeling transient groundwater flow under imprecisely known parameters using fuzzy approach. A new approach has been developed to study the effects of parameters uncertainty on the dependent variable, herein is the head. The proposed approach is developed based on fuzzy set theory combined with interval analysis. The kind of uncertainty modeled here is the imprecision associated with model parameters as a result of machine or human imprecision or lack of information. In this technique each parameter is described by a membership function. The fuzzy inputs into the model are in the form of intervals so as the outputs. The resulting head interval represents the change in the output due to interval inputs of model parameters. The proposed technique is illustrated using a two dimensional flow problem solved with finite difference schemes using triangular and trapezoidal fuzzy membership functions. Three input parameters are considered as a fuzzy number (transmissivity, storage coefficient, and recharge). This model was applicable for transient flow through isotropic, heterogeneous soil. The groundwater flow problem analysis requires interval input values for the parameters, the output may be presented in terms of mean value, upper and lower bounds of the hydraulic head. The width of the resulting head interval can be used as a measure of uncertainty due to inputs imprecision. The model compared with other models (fuzzy with finite difference, stochastic, and Kriging), analytical solution for examples, and then applied to the field data (Bahr Al-Najaf, a case study in Iraq), the proposed technique shows good results. When more than one parameter are considered as a fuzzy number, the condition became more complicated and the uncertainty will increase, that was really shown by the proposed model. The model outputs can be used as the inputs for the subsequent risk analysis, decision making-processes and evaluation.

Highlights

  • Modeling groundwater flow requires the determination of various hydrogeological parameters such as transmissivity, storativity, aquifer thickness, effective recharge and boundary conditions

  • The method presented in this paper describes the use of imprecise data in transient groundwater flow simulation using fuzzy set theory (Appendix A)

  • When using storage coefficient and transmissivity as a fuzzy numbers the uncertainty which equal to 6.9732 m at t = 1322 sec was still less than the uncertainty of aforementioned reference (Dou, 1997) which equal to 7.5 m at t = 1000 sec

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Summary

Introduction

Modeling groundwater flow requires the determination of various hydrogeological parameters such as transmissivity, storativity, aquifer thickness, effective recharge and boundary conditions. This entails the estimation of fuzzy data from alternative hydrogeological parameters, the sampling of realizations from fuzzy hydraulic conductivity data, including, most importantly, the appropriate aggregation of expert-provided fuzzy hydraulic conductivity estimates with traditionally-derived hydraulic conductivity measurements, and utilization of this information in the numerical simulation of groundwater flow and transport He et al (2007) developed an integrated fuzzy-stochastic risk assessment (IFSRA) approach to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The results show that (1) the computational cost using the nonlinear model is reduced compared with that of using the sparse grid algorithm and Monte Carlo methods; (2) the uncertainty of hydraulic conductivity (K) significantly influences the water head and solute distribution at the observation wells compared to other uncertain parameters, such as the storage coefficient and the distribution coefficient (K(d)); and (3) the combination of multiple uncertain parameters substantially affects the simulation results. Kumar, Neetu, and Bansal (2011) presented a new method to solve a fully fuzzy linear equations with arbitrary coefficients, i.e. including negative values for fuzzy coefficients for triangular fuzzy numbers

Methodology
Solution Formulation
Model Verification
Non Uniform Boundary Flow Field
Comparison the Proposed Model with Stochastic and Kriging Models
Discussion and Conclusions
Interval Arithmetic
Triangular Fuzzy Number

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