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Water 2016, 8(7), 287; doi:10.3390/w8070287

Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow

1
Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
2
Department of Civil Engineering, University of Sulaimani, Sulaimani 46001, Iraq
3
Physical Sciences Division, Department of Statistics, University of Chicago, Chicago, IL 60637, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Y. Jun Xu
Received: 11 April 2016 / Revised: 6 July 2016 / Accepted: 8 July 2016 / Published: 13 July 2016
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Abstract

Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS), were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT) model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed) and local (subbasin) results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales. View Full-Text
Keywords: streamflow; SWAT; ANFIS; Bayesian regression; soft computing streamflow; SWAT; ANFIS; Bayesian regression; soft computing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hamaamin, Y.A.; Nejadhashemi, A.P.; Zhang, Z.; Giri, S.; Woznicki, S.A. Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow. Water 2016, 8, 287.

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