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

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.

Assessment of the effective width of riparian buffer strips to reduce suspended sediment in an agricultural landscape using ANFIS and SWAT models

“…Data-driven models and physical process-based models have different strengths and limitations for estimating suspended sediment concentrations (SSC) and sediment loads. Despite their relatively easy implementation for examining impacts of changes in landscape management practices, physical process-based models can require a large number of inputs, adjustment of numerous parameters and high computational time during calibration, compared to data-driven models (Hamaamin et al, 2016). However, the potential of data-driven models for exploring the implications of altering factors that may influence erosion, such as land-use/landcover in agricultural watersheds, remains relatively unexplored.…”

Distributed Hydrological Modeling: Determination of Theoretical Hydraulic Potential & Streamflow Simulation of Extreme Hydrometeorological Events

“…An RSR of zero indicates the optimal value, while RSR > 0.7 represents unsatisfactory model performance [38] as shown in Table 1.…”