为提高堤防工程风险评价的准确性,提出了基于极限学习机的堤防工程多元风险指标评价方法。首先,综合考虑影响堤防风险的28个评价指标,利用层次分析法从预警系统、堤防工程系统、环境系统和社会经济系统这4个方面建立了堤防工程多元风险评价指标体系。接着,基于极限学习机算法对28个指标进行标准化处理及分级标准构建,以风险指标作为输入量,分级隶属度作为输出量,划分风险等级,量化评价指标,估计多元风险评价指标值和判断风险的严重程度。最后,依托鄱阳湖重点堤防——康山大堤,构建多元风险评价指标体系,运用极限学习机算法计算多元风险评价指标值。评价结果表明:康山大堤目前处于基本安全水平,符合康山大堤经过两次加固后的工程实际情况,并与其他方法进行对比,验证了提出方法的可靠性和有效性。该方法可拓展应用到其他重要水工结构工程风险评估中。
Abstract
To improve the accuracy of risk assessment for levee engineering, we present a multi-risk index evaluation approach for levee engineering based on extreme learning machine. First of all, 28 evaluation indices that affect the levee risk are comprehensively considered, and the analytic hierarchy process is adopted to establish a multi-risk index evaluation system which comprises of early warning system, levee engineering system, environmental system and social economic system. Subsequently, the extreme learning machine algorithm is employed for standardized processing of the 28 indices and constructing grading standards. With the risk indices and the grading membership as the input and output, respectively, the risk levels are divided, the evaluation indices are quantified, the values of multiple risk evaluation indices are estimated, and the risk severity is judged. With Kangshan levee, a key levee of Poyang Lake, as a case study, the present multi-risk evaluation index system is established and the values of multi-risk evaluation indices are computed using the extreme learning machine algorithm. The evaluation results demonstrate that the Kangshan levee is safe in general, which is in line with the actual situation of the Kangshan levee after two reinforcements. The reliability and effectiveness of the proposed method is verified by comparison with other methods. The proposed approach is expected to be applied to the risk assessment of other important hydraulic structures.
关键词
堤防工程 /
风险评价 /
层次分析法 /
极限学习机 /
多元指标体系
Key words
levee engineering /
risk assessment /
analytic hierarchy process /
extreme learning machine /
multi-index system
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基金
国家重点研发计划重点专项(2016YFC0402500);国家自然科学基金项目(41867036);江西省自然科学基金项目(2018ACB21017);江西省水利厅科技项目(202022YBKT03,202123YBKT12,KT201534)
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