基于SBO-PP模型的水资源可持续发展能力评价模型及应用

为利用投影寻踪(PP)技术科学,客观评价区域水资源可持续发展能力,基于生态环境、水资源和经济社会因素选取20个指标构建区域水资源可持续发展能力评价指标体系。利用新型仿生群体智能算法——凉亭鸟优化(SBO)算法优化PP技术最佳投影方向,提出SBO-PP水资源可持续发展能力评价模型,并构建灰狼优化(GWO)算法-PP模型、人工蜂群(ABC)算法-PP模型、差分进化(DE)算法-PP模型、地理生物学优化(BBO)算法-PP模型、粒子群优化(PSO)算法-PP模型作对比,以云南省2006—2015年及2020年水资源可持续发展能力评价为例进行实例研究。结果表明:SBO算法优化PP技术获得的适应度值优于GWO、ABC、DE、BBO和PSO算法,具有较好的求解精度、稳健性能和极值寻优能力。SBO-PP模型对云南省2006—2007年水资源可持续发展能力评价为"较差",2008—2015年评价为"中等",2020年评价为"较强"。

Evaluationmodel for sustainable development capacity of water resources based on SBO-PP model and its application

In order to utilize the projection pursuit(PP)technique to scientifically and objectively evaluate the sustainable development capacity of regional water resources, an evaluation index system is established by selecting 20 indices based on the ecological environment, water resources and economic and social factors. By using the new bionic swarm intelligence algorithm, satin bower bird optimization(SBO)algorithm to optimize the best projection direction of PP technique, the evaluation model SBO-PP for the sustainable development capacity of water resources is proposed, and the gray wolf optimization(GWO)algorithm-PP model, the artificial bee colony(ABC)algorithm-PP model, the differential evolution(DE)algorithm-PP model, the biogeography-based optimization(BBO)algorithm-PP model, the particle swarm optimization(PSO)algorithm-PP model are compared, and the sustainable development capacities of water resources in Yunnan Province from 2006 to 2015 and in 2020 are evaluated. The results show that the SBO algorithm is better than the algorithms of GWO, ABC, DE, BBO and PSO in the optimization of PP technique, and it has better capability of solving precision, robust performance and extreme value. The sustainable development capacity of water resources in Yunnan from 2006 to 2007 evaluated by the SBO-PP model is worse, that from 2008 to 2015 is moderate, and that in 2020 is strong.