Title: | Application of multimodal optimization for uncertainty estimation of computationally expensive hydrologic models |
Authors: | Cho, H. and F. Olivera |
Year: | 2014 |
Journal: | Journal of Water Resources Planning and Management |
Volume (Issue): | 140(3) |
Pages: | 313–321 |
Article ID: | |
DOI: | 10.1061/(ASCE)WR.1943-5452.0000330 |
URL (non-DOI journals): | |
Model: | SWAT |
Broad Application Category: | hydrologic and pollutant |
Primary Application Category: | calibration, sensitivity, and/or uncertainty analysis |
Secondary Application Category: | sediment loss and transport |
Watershed Description: | 598 km^2 Big Sandy Creek in northeast Texas, U.S. |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
Abstract: | The Generalized Likelihood Uncertainty Estimation (GLUE) framework has been widely used in hydrologic studies. However, an extensive random sampling causes a high computational burden, which prohibits the efficient application of GLUE to costly distributed hydrologic models such as the Soil and Water Assessment Tool (SWAT). In this study, a multi-modal optimization algorithm called Isolated-Speciation-based Particle Swarm Optimization (ISPSO) is employed to take samples from the search space. A comparison between the ISPSO-GLUE, proposed here, and traditional GLUE approaches shows that the two approaches generate similar uncertainty bounds, but that the convergence rate to stable uncertainty bounds is much faster for ISPSO-GLUE than for GLUE. That is, ISPSO-GLUE needs a much smaller number of samples than GLUE to arrive to a very similar answer. Although the ISPSO-GLUE slightly underestimated the prediction uncertainty and missed a number of observed values, the proposed approach is considered to be a good alternative to the typical GLUE approach that employs random sampling. |
Language: | English |
Keywords: | |