
科学技术与工程

中国技术经济学会
基于WOA-SA-RBF模型的西北内陆河流域突发水污染安全评价
靳春玲 , 田亮 , 贡力 , 李战江 , 蔡惠春
科学技术与工程 ›› 2025, Vol. 25 ›› Issue (23) :10075-10083
基于WOA-SA-RBF模型的西北内陆河流域突发水污染安全评价
Safety Evaluation of Sudden Water Pollution in Northwest Inland River Basin Based on WOA-SA-RBF Model
为保障西北内陆河流域生态安全,急需开展西北地区内陆河流域突发水污染安全评价。聚焦于疏勒河流域敦煌区域,通过运用压力-状态-响应(pressure-state-response, PSR)模型框架,基于2017—2022年该流域的历史数据,采用一种融合鲸鱼优化与模拟退火策略的径向基(whale optimization algorithm-simulated annealing-radial basis function, WOA-SA-RBF)神经网络模型,来评估该区域的突发水污染风险等级,并与粒子群优化算法-径向基(particle swarm optimization-radial basis function, PSO-RBF),遗传优化算法-径向基(genetic algorithm-radial basis function, GA-RBF)神经网络模型及传统评价方法优劣解距离法(technique for order preference by similarity to ideal solution, TOPSIS)法的评价结果进行对比分析。分析结果显示:疏勒河敦煌段在2017—2018年突发水污染风险水平被评定为Ⅱ级,而2019—2022年则降为Ⅲ级,显示出风险逐渐下降并趋向稳定的趋势;结果与TOPSIS法分析结果一致,与流域治理情况相符,从而有效验证本文评估模型的精度。研究成果有助于提高疏勒河流域针对突发水污染事件的预防控制能力与紧急应对效率,对西北内陆河流域的水资源管理以及祁连山区域的生态保护工作具有不可忽视的重要意义。
In order to ensure the ecological security of inland river basins in northwest China, it is urgently needed to conduct assessments on the safety of sudden water pollution in inland river basins in northwest China. This study focuses on the Dunhuang area of the Shule River Basin. By applying the pressure-state-response (PSR) model framework and based on historical data from 2017 to 2022 in this basin, a radial basis function (RBF) neural network model integrated with whale optimization algorithm-simulated annealing (WOA-SA) was used to assess the risk level of sudden water pollution in this area. The assessment results are compared with those obtained from particle swarm optimization-RBF (PSO-RBF), genetic algorithm-RBF (GA-RBF) neural network models, and the traditional Technique for order preference by similarity to ideal solution (TOPSIS) method. The analysis results show that the risk level of sudden water pollution in the Dunhuang section of the Shule River is rated as Grade Ⅱ between 2017 and 2018, and then decrease to Grade Ⅲ from 2019 to 2022, indicating a trend of gradually decreasing and stabilizing risks. The results are consistent with those of the TOPSIS method and align with the basin management situation, effectively validating the accuracy of the assessment model. The research findings are helpful in improving the prevention and control capabilities and emergency response efficiency of the Shule River Basin in response to sudden water pollution incidents. They are of great significance to water resources management in northwest inland river basins and ecological protection in the Qilian Mountain area.
鲸鱼优化算法(WOA) / 模拟退火算法(SA) / 径向基神经网络模型(RBF) / 突发水污染 / 安全评价 / 内陆河
whale optimization algorithm (WOA) / simulated annealing algorithm (SA) / radial basis neural network model(RBF) / sudden water pollution / security assessment / inland river
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