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1.
Afshin Shabani Xiaodong Zhang Xuefeng Chu Timothy P. Dodd Haochi Zheng 《Journal of the American Water Resources Association》2020,56(2):297-309
Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m3/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L. 相似文献
2.
What matters for work engagement? A diary study on resources and the benefits of selective optimization with compensation for state work engagement 下载免费PDF全文
This diary study addresses the benefits of employees' daily use of selective optimization with compensation (SOC) for state work engagement. We hypothesized that day‐level SOC not only directly fosters work engagement but that SOC also reveals its beneficial effects for work engagement in interaction with both external and internal resources. Specifically, we proposed SOC substitutes for job control, role clarity, and state of being recovered, thus helping employees manage low daily levels of these resources. We tested our hypotheses with a sample of 138 employees who completed two daily surveys over a total of 545 workdays. Results of multilevel analyses revealed that SOC benefits work engagement in both proposed ways. First, day‐level SOC was positively related to state work engagement. Additionally, day‐level role clarity and state of being recovered predicted state work engagement, but day‐level job control did not. Second, SOC benefitted state work engagement by offsetting low levels of role clarity and being recovered, and by boosting job control in their respective relationships with work engagement. The results suggest that by using SOC at work, employees can actively enhance their own work engagement on a given workday. This knowledge provides promising starting points for the development of interventions. 相似文献
3.
研究了几种典型的城市给水管网优化设计数学模型,并给出了求解方法,指出在给水管网系统的优化设计中进一步完善广义简约(GRG)算法的必要性. 相似文献
4.
ABSTRACT: Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag-one Markov process. To test the usefulness of both models in generating reservoir operating rules, real-time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs. 相似文献
5.
本文在分析世界博览会发展过程的基础上,阐述了2010年上海世博会对上海乃至长江三角洲都市圈联动发展的机遇和挑战,并提出了长江三角洲都市圈空间整合与联动发展的战略措施。 相似文献
6.
面向国土空间规划的科学性、可操作性、层级性等内涵,探讨了资源环境承载能力评价和国土空间开发适宜性评价(“双评价”)与国土空间规划之间的基本逻辑问题、应用挑战与应对方法。通过系统梳理“双评价”的理论发展和相关政策响应,剖析了“双评价”在应用探索、内涵扩充、系统支撑三个阶段的研究重点和服务目标,阐明了承载力评价与适宜性评价之间关联逻辑的演进历程。总结了当前“双评价”在应用上的几个关键挑战:一是“双评价”应用于“三区三线”划定的逻辑尚未明确;二是当前承载能力评价与未来规划决策之间存在逻辑悖论;三是“双评价”在不同层级国土空间规划中传导失灵;四是两个评价之间的关联逻辑仍存在争议。为此,提出了应对“双评价”挑战的四点建议:深化理论认知、拓展评价维度、建立传导机制、厘清内在关系,以提升“双评价”对国土空间规划决策的支撑能力。 相似文献
7.
我国城市建设和生态保护工作均对土地资源有大量需求,二者之间的矛盾在经济优先发展区表现尤为明显.为了有效地改善生态环境,管控土地利用并引导其变化发展,需要建设具备不可替代特征的省域生态廊道.最小累积阻力模型(minimum cumulative resistance,MCR)是识别生态廊道最常用、有效的模型,但在应用于省域尺度时,MCR模型识别的潜在廊道路由存在冗余的问题.因此,通过引入网络科学中的边介数指数(edge-betweenness)对MCR模型进行优化,计算潜在廊道路由的边介数指数值,选取出其中最为重要和简明的结构来连通生态源地,即提取潜在路由中的骨干路由(backbone route)和关键战略点(key strategic point)作为不可替代的结构来指导省域生态廊道建设.将优化后的MCR模型应用于广东省,构建了全长5 493 km的省域生态廊道,其中包含生态源地20处,关键战略点11个,骨干生态廊道29条.骨干路由与关键战略点构成的不可替代省域生态廊道(irreplaceable provincial corridor)能够实现"廊道数量和占地面积最少、连通性基本不变"的目标.研究显示,边介数能够对潜在路由进行优化筛选,识别出维护省域生态安全的关键结构;不可替代生态廊道能够指导省域生态规划和土地空间的发展利用,并为更高水平的生态安全环境提供了演进的基础;同时也为土地资源紧张的地区提供了建设生态廊道的参考与依据. 相似文献
8.
新时代国土整治与生态修复转型思考 总被引:4,自引:1,他引:3
针对全球变化影响下的国土空间和生态系统,生态文明建设和国土空间规划是中国在新时代的积极响应,国土整治与生态修复的转型和提升成为必然要求。在介绍国土整治与生态修复概念内涵的基础上,指出中国国土空间生态环境问题的复杂性和生态文明建设的新理念要求决定了国土整治与生态修复的转型。从工作理念、理论基础、技术体系和制度建设等方面,分析了当前国土整治与生态修复工作中存在的不足,主要包括整体综合理念滞后、理论基础体系欠缺、技术支撑相对薄弱、体制机制不尽完善等方面。针对这些不足,提出了新时代国土整治与生态修复转型的路径和策略,主要策略包括强化系统思维、提升理论体系、加强技术支撑、完善机制建设等内容,以期为国土整治与生态修复推进美丽中国建设提供科学依据。 相似文献
9.
Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization 总被引:6,自引:0,他引:6
Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems. 相似文献
10.