Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic

Authors:
Roohollah Noori;Amir Khakpour;Babak Omidvar;Ashkan Farokhnia
Affiliations:
Department of Water Resources Research, Institute of Water Researches, Ministry of Energy, Tehran, Iran and Department of Environmental Engineering, Graduate Faculty of Environment, University of ...;Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran and Director of Civil, Environmental, Laboratory and Consulting Engineering (CELCO) Com ...;Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran;Department of Water Resources Research, Institute of Water Researches, Ministry of Energy, Tehran, Iran and Department of Water Resources Engineering, Kerman Graduate University of Technology, Ker ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Predicting the stream flow is one of the most important steps in the water resources management. Artificial neural network (ANN) has been suggested and applied for this purpose by many of researchers. In such studies for verification and comparison of ANN results usually the popular methods such as multivariate linear regression (MLR) is used. Unfortunately, the presented methodology in some researches is faced with some problems. Thus, in this paper we have tried to find out the deficiencies of them and subsequently to present a correct the MLR methodology based on principal component analysis (PCA) for prediction of monthly stream flow. Then, assessment of different training functions on ANN operation is investigated and the best training function for optimizing the ANN parameters is selected. Afterward, the imperfections of the discrepancy ration (DR) statistic are remedied and a proper DR statistic is developed. Finally, the error distribution for testing stage of MLR and ANN models are calculated using developed DR statistic. The results of comparison show that the presented methodology in this research has improved the MLR operation. Also, comparing with the MLR, the ANN model possesses satisfactory predicting performance.