Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods

Sinan Jasim Hadi () and Mustafa Tombul ()
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Sinan Jasim Hadi: Anadolu University
Mustafa Tombul: Anadolu University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 10, No 10, 3405-3422

Abstract: Abstract Modelling streamflow is essential for activities, such as flood control, drought mitigation, and water resources utilization and management. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM) are techniques that are frequently used in hydrology to specifically model streamflow. This study compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow with the traditional approach known as autoregressive (AR) model for basins with different physical characteristics. The accuracies of the models are compared for three basins, that is, 1801, 1805, and 1822, at the Seyhan River Basin in Turkey. The comparison was performed by using coefficient of efficiency, index of agreement, and root-mean-square error. Results indicate that ANN and ANFIS are more accurate than AR and SVM for all the basins. ANN and ANFIS perform similarly, while ANN outperformed ANFIS. Among the models used, the ANN demonstrates the highest performance in forecasting the peak flood values. This study also finds that physical characteristics, such as small area, high slope, and high elevation variation, and streamflow variance deteriorate the accuracy of the methods.

Keywords: Artificial neural network; Adaptive neuro-fuzzy inference system; Support vector machines; Autoregression; Streamflow (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-1998-1

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