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Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic
Author(s) -
Toprak Zeynel Fuat,
Eris Ebru,
Agiralioglu Necati,
Cigizoglu Hikmet Kerem,
Yilmaz Levent,
Aksoy Hafzullah,
Coskun Hilal Gonca,
Andic Gokhan,
Alganci Ugur
Publication year - 2009
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.200800152
Subject(s) - structural basin , fuzzy logic , flow (mathematics) , estimation , hydrology (agriculture) , environmental science , geology , computer science , mathematics , engineering , artificial intelligence , geomorphology , geotechnical engineering , geometry , systems engineering
The estimation of the monthly mean flow is a critical issue in many water resource development projects. However, in practice the mean flow is not easily determined in ungauged and poorly gauged basins. Therefore, in the literature, various flow estimation methods have been developed recently for mountainous regions which are generally ungauged or poorly gauged basins. In this study a fuzzy logic model based on the Mamdani approach was developed to estimate the flow for poorly gauged mountainous basins. This model was applied to the Solakli Basin which is located in the Eastern Black Sea Region of Turkey. Limited rainfall and flow data are available for this basin. In addition to these variables, the stream and time coefficients were introduced and used as variables for modeling. The data was divided into training and testing phases. The model results were compared with the measured data. The comparison depends on seven statistical characteristics, four different error modes and the contour map method. It was observed that the fuzzy model developed in this study yielded reliable results.