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Abstract
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Article Information:
Application of Adaptive Neuro Fuzzy Inference System (Anfis) in River Kaduna Discharge Forecasting
J.O. Folorunsho, E.O. Iguisi, M.B. Mu’azu and S. Garba
Corresponding Author: J.O. Folorunsho
Submitted: February 02, 2012
Accepted: March 02, 2012
Published: November 01, 2012 |
Abstract:
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The study and understanding of the amount of water that would be discharged by a stream in the
future is of crucial importance to the water resources development, planning and management of any area. This
is so because, stream flow data are very important for many areas of water engineering. The data used for this
research include the monthly rainfall data for Kaduna town, Zaria and Jos, temperature data for Kaduna town,
relative humidity for Kaduna town and the stage height data for the studied river and the discharge data. All
the data used span the period 1975-2004 (30 years). These parameters are non-linear, stochastic (random) and
uncertain in nature. Adaptive Neuro-Fuzzy based Inference System (ANFIS), an integrated system, comprising
of Fuzzy Logic and Neural Network was used to model the discharge forecast, because it can address and solve
problems related to non-linearity, randomness and uncertainty of data. The ANFIS-based model developed uses
70% of data for training and 30% for checking; subsequently validation data of the variables were used to
predict the discharge and test the model developed. From the analysis carried out on the ANFIS-based model;
Root Mean Square Error (RMSE) found to be 107.62. The analysis shows high level of accuracy with regards
to the ANFIS-based model developed in forecasting the river discharge especially with a correlation (r) value
of 86%.
Key words: ANFIS, river discharge, RMSE, , , ,
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Cite this Reference:
J.O. Folorunsho, E.O. Iguisi, M.B. Mu’azu and S. Garba, . Application of Adaptive Neuro Fuzzy Inference System (Anfis) in River Kaduna Discharge Forecasting. Research Journal of Applied Sciences, Engineering and Technology, (21): 4275-4283.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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