北京师范大学学报（自然科学版）2016，Vol.52Issue(3)：297-302,6.DOI:10.16360/j.cnki.jbnuns.2016.03.008
蚁群算法在黑河上游VIC模型参数校正中的应用
Ant colony optimization in parameter calibration for the variable infiltration capacity (VIC)model in the upper Heihe River basin
摘要
Abstract
Parameter calibration is a fundamental task for the application of hydrological models.Ant colony optimization (ACO)algorithm is a meta-heuristic algorithm and it shows a strong ability in tackling combinatorial problems, suitable for hydrological model calibration. In this study, ACO was applied to parameter calibration of variable infiltration capacity (VIC)model in the upper Heihe River basin,China. Shuffled complex evolution algorithm (SCE-UA)was used to test applicability of ACO.It is found that the ACO is capable of model calibration for VIC.Nash—Sutcliffe coefficient of efficiency is 0.62 in calibration period,and 0.65 in validation period,rather similar to SCE-UA results.The strategies of ACO are also discussed.Influence of the two most sensitive parameters of ACO is further investigated.The best performance of ACO is achieved when ant number is 60 and pheromone evaporation rate is 0.2.It is concluded that ACO is an effective global optimization method to calibrate large scale hydrological model.This method is also suitable for other hydrological models.关键词
参数优选/蚁群算法/VIC模型/黑河上游Key words
parameter calibration/ant colony optimization algorithm/VIC model/upper Heihe River basin分类
建筑与水利引用本文复制引用
岳佳佳,庞博,徐宗学,何睿..蚁群算法在黑河上游VIC模型参数校正中的应用[J].北京师范大学学报（自然科学版）,2016,52(3):297-302,6.基金项目
国家自然科学基金资助项目(91125015,51309009) （91125015,51309009）
