| 基于足球联赛竞争算法-投影寻踪-云模型的水资源短缺风险评价 |
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| 引用本文： | 李菊,崔东文,袁树堂.基于足球联赛竞争算法-投影寻踪-云模型的水资源短缺风险评价[J].水文,2018,38(4):40-47. |
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| 作者姓名： | 李菊 崔东文 袁树堂 |
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| 作者单位： | 云南开放大学;云南省文山州水务局;云南省水文水资源局 |
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| 摘 要： | 为客观衡量水资源短缺风险评价过程中的随机性与模糊性,将正态云模型引入水资源短缺风险评价,建立足球联赛竞争算法-投影寻踪-正态云评价模型,以云南省各州市水资源短缺风险评价为例进行实例研究。从水资源等系统遴选20个指标构建水资源短缺风险评价指标体系和分级标准,采用云模型正向发生器计算水资源短缺风险分级评价指标的隶属度;在分级标准阈值间随机内插样本构造投影寻踪优化目标函数,利用足球联赛竞争算法、粒子群算法、布谷鸟搜索算法和差分进化算法优化投影寻踪目标函数并进行比较,并通过足球联赛竞争算法-投影寻踪法给出各指标权重;根据隶属度矩阵和权重矩阵计算水资源短缺风险评价的分级确定度并进行评价,评价结果与投影寻踪法、模糊综合评价法比较。结果表明:足球联赛竞争算法寻优精度高于粒子群算法等3种算法。昆明市、怒江州和迪庆州水资源风险评价为低风险;玉溪市、保山市、文山州和德宏州评价为较低风险;曲靖市、昭通市、普洱市、临沧市和红河州评价为中等风险;其余州市评价为较高风险,评价结果与投影寻踪法、模糊评价法基本一致。足球联赛竞争算法-投影寻踪-正态云评价模型兼具模糊性和随机性,既能反映水资源短缺风险评价分级的定性概念,又可反映隶属程度的不确定性,具有较好的应用价值。
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| 关 键 词： | 水资源短缺 风险评价 正态云模型 指标体系 足球联赛竞争算法 投影寻踪 |
| 收稿时间： | 2018/1/12 0:00:00 |
Evaluation of Water Resources Shortage Risk Based on Soccer League Competition Algorithm-Projection Pursuit-Cloud Model |
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| LI Ju,CUI Dongwen,YUAN Shutang.Evaluation of Water Resources Shortage Risk Based on Soccer League Competition Algorithm-Projection Pursuit-Cloud Model[J].Hydrology,2018,38(4):40-47. |
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| Authors: | LI Ju CUI Dongwen YUAN Shutang |
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| Institution: | 1. Yunnan Open University, Kunming 650223, China;2. Wenshan Water Affairs Bureau of Yunnan Province,Wenshan 663000, China;3. Yunnan Bureau of Hydrology and Water Resources,Kunming 650106,China |
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| Abstract: | To objectively measure the randomness and ambiguity in the process of water scarcity risk assessment, the normal cloud model wasintroduced to the assessment of water scarcity risk, and a competition algorithm for football leagues-projection pursuit-normal cloud evaluationmodel was established to use for the various cities in Yunnan Province. Water shortage risk assessment was used as an example to study. Twentyindicators were selected from the water resources system to establish the water scarcity risk assessment index system and grading standards, andthe cloud generator was used to calculate the degree of membership of the water shortage risk grading evaluation index; the sample structure wasrandomly interpolated between the grading standard thresholds. Projection pursuit optimizes the objective function, uses the football leaguecompetition algorithm, particle swarm algorithm, cuckoo search algorithm and differential evolution algorithm to optimize the projection pursuitobjective function and compare them, and gives each index through the soccer league competition algorithm -projection pursuit method.According to the degree of membership matrix and weight matrix to calculate the degree of water shortage risk assessment of the degree of certaintyand make evaluation, the results were compared with the projection pursuit method and fuzzy comprehensive evaluation method. The results showthat the accuracy of the soccer league competition algorithm is higher than that of the particle swarm algorithm. Risk assessments for waterresources in Kunming, Nujiang, and Diqing indicate a low risk; Yuxi, Baoshan, Wenshan, and Dehong were assessed as lower risk; Qujing,Zhaotong, Puer, Linyi, and Honghe were assessed as moderate risk; the other cities were rated as higher risk, and the evaluation results werebasically consistent with the projection pursuit method and fuzzy evaluation method. The football league competition algorithm -projectionpursuit-normal cloud assessment model is both fuzzy and random, it can not only reflect the qualitative concept of water shortage risk assessmentrating, but also reflect the uncertainty of the degree of membership. |
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| Keywords: | water resources shortage risk assessment normal cloud model index system soccer league competition algorithm projection pursuit |
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