Multi-objective trade-offs

多目标权衡
  • 文章类型: Journal Article
    水坝(水库)引起的水流和水温变化可能威胁下游鱼类栖息地和本地水生生态系统。减轻大坝-水库对环境的负面影响,平衡水库运行的多种目的已引起广泛关注。尽管先前的研究已将生态流量要求纳入水库运营策略中,综合分析水电效益之间的取舍,生态流,缺乏生态水温需求。因此,本研究建立了多目标生态调度模型,考虑到总发电量,生态流量保障指数,与生态水温保障指标同步。该模型基于应用于三峡水库的集成多目标模拟优化(MOSO)框架。为此,首先,利用混合长短期记忆和一维卷积神经网络(LSTM_1DCNN)模型来模拟大坝泄流温度。然后,提出了一种改进的ε多目标蚁群优化连续域算法(ε-MOACOR)来研究竞争目标之间的权衡。结果表明,LSTM_1DCNN在预测大坝泄水温度方面优于其他竞争模型。经济和生态目标之间的冲突往往很突出。提出的ε-MOACOR具有解决此类冲突的潜力,并且在解决多目标基准测试以及水库优化问题方面具有很高的效率。更现实和务实的帕累托最优解的典型干燥,MOSO框架可以产生正常和潮湿的年份。生态水温保障指标目标,这应该在水库运行中考虑,可以随着入流流量的增加或大坝流量的时间分布变得更加不均匀而改善。
    Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.
    导出

    更多引用

    收藏

    翻译标题摘要

    我要上传

    求助全文

  • 文章类型: Journal Article
    具有成本效益的径流控制方案起草涉及本地化,多部门协调,和多功能基础设施的配置。许多独立变量,参数,重量,和目标使径流控制优化在数量上艰巨。本研究创新性地提出了一种适应自然禀赋和城市发展空间异质性的绿灰色耦合径流控制基础设施多目标优化方法。多目标评价的定量方法,水文特征分区,提出了压力自适应多目标权重分配方法。水质遥感反演,水文模型模拟(使用SWAT和SWMM软件),景观格局指数计算,生命周期成本(LCC),对生态影响的生命周期评估(LCA),并应用NSGA-II优化算法。武汉,中国对水最敏感的城市,作为一个案例进行了研究。径流控制功能(RCF),资本投资(CI),和生态投资回报(EROI)作为优化目标。High,中等,利用地形因子和景观格局提取武汉市城市发展规划区低建成区,占病例面积的28%,34%和38%,分别。然后建立了三个相应的水文模型,以说明每个地区不同的径流控制成本效率。径流控制的压力分布,经济约束,并对生态资源稀缺性进行了定量评价。四个压力区聚集在一起,占病例面积的36%,29%,16%和19%,分别。然后通过叠加压力区和建成区,建立了区域加权优化决策矩阵(具有3个水文模型和5wt)。在高,中等,和低建筑区,优化的解决方案将年径流量减少了86%,82%,和77%的高不透水下垫面每平方米的平均径流投资,中等,低建成区分别为34.2、18.7和7.9元,分别。中,低建筑区域可能只需要高建筑区域的55%和23%即可获得整体不透水的下垫面,以平衡径流控制和生态效益。径流控制和财政利用效率随着水文分化带的提高而提高。因此,优化方案是区域自适应的,精致,可比性,可复制,并且可实施。
    Cost-effective runoff control scheme drafting involves localization, multi-sector coordination, and configuration of multifunctional infrastructures. Numerous independent variables, parameters, weights, and objectives make runoff control optimization quantitatively arduous. This study innovatively proposed a multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development. The quantitative methods of multi-objective evaluation, hydrological feature partition, and pressure-adapted multi-objective weight assignment were proposed. Remote sensing inversion of water quality, hydrological model simulation (using SWAT and SWMM software), landscape pattern index calculation, life cycle cost (LCC), life cycle assessment (LCA) on ecological impact, and NSGA-II optimization algorithm were applied. Wuhan, the most water-sensitive city in China, was studied as a case. Runoff control function (RCF), capital investment (CI), and ecological return on investment (EROI) served as optimized objectives. High, medium, and low built-up regions in Wuhan urban development planning district were extracted by topographic factors and landscape patterns, which comprised 28, 34, and 38% of the case area, respectively. Three corresponding hydrological models were then built to illustrate distinct runoff control cost-efficiency in each region. Pressure distributions on runoff control, economic constraints, and ecological resource scarcity were quantitatively evaluated. And four pressure zones were clustered, which occupied 36, 29, 16, and 19% of the case area, respectively. Then the zonal weighted optimization decision-making matrix (with 3 hydrological models and 5 wt) was established by overlaying the pressure zone and built-up zone. In high, medium, and low built-up regions, optimized solutions reduced annual runoff volume by 86, 82%, and 77%The average runoff investments per square meter of impervious underlying surface in high, medium, and low built-up regions were 34.2, 18.7, and 7.9 RMB yuan, respectively. Medium and low built-up regions may only need 55 and 23% of the high built-up region for the unitary impervious underlying surface to balance runoff control and ecological benefits. Runoff control and financial utilization efficiency enhance with hydrological differentiation zones. Thus, the optimization solutions are zonal adaptive, refined, comparable, replicable, and implementable.
    导出

    更多引用

    收藏

    翻译标题摘要

    我要上传

    求助全文

公众号