Premium
A predictive model for well loss using fuzzy logic approach
Author(s) -
Altunkaynak Abdüsselam
Publication year - 2010
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.7642
Subject(s) - fuzzy logic , mean squared error , normality , computer science , aquifer , mathematical optimization , mathematics , statistics , groundwater , geology , artificial intelligence , geotechnical engineering
Abstract Simple methods for calculating well losses are important for well design and optimization of groundwater source operation. Well losses arise from both laminar flow within the aquifer and turbulent flow within the well, and are often ignored in theoretical aquifer test analysis. The Jacob (1947) and Rorabaugh (1953) techniques for predicting well losses are widely used in the literature; however, inherent in these techniques are the assumptions of linearity, normality and homoscedascity. In the Rorabaugh technique, prior knowledge, or prediction of, the parameters A , C and n is required for calculation of well losses. Unfortunately, as of yet, no method for adequately obtaining these parameters without experimental data and linear regression exist. For these reasons, the Rorabaugh methodology has some practical and realistic limitations. In this paper, a fuzzy logic approach is employed in the calculation of well losses. An advantage of the fuzzy logic approach is that it does not make any assumptions about the form of the well loss functionality and does not require initial estimates for the calculation of well losses. Results show that the fuzzy model is a practical alternative to the Rorabaugh technique, producing lower errors (mean absolute error, mean square error and root mean square error) relative to observed data, for the case presented, comparatively to the Rorabaugh model. Copyright © 2010 John Wiley & Sons, Ltd.