Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models

作者：Cho Huidae; Olivera Francisco^*

来源：Journal of Water Resources Planning and Management, 2014, 140(3): 313-321.

DOI：10.1061/(ASCE)WR.1943-5452.0000330

• Uncertainty principles
• Optimization
• Hydrologic models
• Computation
• Hydrology
• Uncertainty
• Particle swarm optimization
• GLUE
• SWAT

The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies. However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment tool (SWAT). In this study, a multimodal 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 at a very similar answer. Although 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.

• 出版日期2014-3-1