Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

粒子群优化算法在源强反算问题中的应用研究

在危险化学品泄漏事故中泄漏源强是预测事故后果的主要影响参数,也是事故应急救援决策的基础。为了在化学品泄漏事故过程中快速准确地获取泄漏源强数据,将粒子群优化(PSO)算法应用于危险化学品泄漏源强的反算中。利用高斯烟羽扩散模型和下风向浓度测量数据,将计算浓度与测量浓度的误差平方和作为目标函数,采用粒子群算法来优化,以确定源强并通过模拟的测量浓度数据进行算法有效性验证。结果表明,PSO算法及其参数改进算法不依赖于初值的选择,计算速度快,能满足事故应急响应救援的需要。     

大型公共场所多源疏散的控制流模型研究

以现有的单源最快流控制算法为基础,考虑人员在大型公共场所中的实际分布状况,遵循最大限度利用各出口的原则,提出针对多源疏散的全局最优化算法。根据此算法可以得出各疏散源点经过各出口的疏散人员数量以及人员的行走路径、从源点出发和完成疏散的时刻。将算法应用于某百货公司的案例研究表明,在多源疏散的情况下,各源点依次按照单源最快流控制算法进行独立疏散,能够保证每个源点的最优疏散,但整体的疏散效率并非最优;而应用全局最优化算法进行疏散,可以充分利用疏散过程中不同出口的疏散时间差,取得减少整体疏散时间、提高疏散效率的效果。     

某整车制造企业清洁生产审核案例分析

以某整车制造企业清洁生产审核项目为例,通过对照涂装行业清洁生产评价指标体系,对该汽车制造企业开展清洁生产审核,结合审核过程中发现的清洁生产潜力,提出方案以提高企业的清洁生产水平。     

A deep model method for recognizing activities of workers on offshore drilling platform by multistage convolutional pose machine

The growing diversity of image scenes brings a great challenge to human activity recognition in practice. Traditional activity recognition methods cannot satisfy the demand of precise action recognition in complex scenes. In this work, we build a training set of worker's activities on offshore drilling platform by collecting data from offshore drilling monitor, and then an improved multi-level convolutional pose machine (MCPM) method is proposed and trained to recognize activities of workers on the platforms. In human object detection, a multi-rule region proposal marker algorithm is developed to separate the seawater area, and the ducts of similar personnel is pre-discriminated by support vector machine. We use the characteristics of the human body key-points not affected by complex background noise to assist the detection of the human target. As results, it shown that our method performs better than Faster-RCNN, MobileNet-SSD and SSD algorithms in detecting human target on the offshore drilling platform, and achieves well accuracy in recognizing many key activities. To our best acknowledge, it is the first attempt of using deep model to recognize worker's activities on offshore drilling platform.     

基于改进CM的尾矿坝体变形4级预警阈值确定方法*

为改善我国尾矿库在线监测系统由于重建设、轻利用而导致的事故预警能力被制约的现状,以尾矿坝体变形速率监测指标为研究对象,通过引入改进云模型,根据3E规则确定尾矿坝变形监测的正常运营值边界,建立尾矿库坝体变形速率4级预警阈值确定模型,以湖北省某尾矿库为例进行实例分析。结果表明:通过提出的坝体预警阈值确定方法,可得出尾矿库坝体变形速率黄、橙、红各级预警阀值分别为10.842,16.68,25.02 mm/d;进一步与典型小概率法计算结果对比表明,所提出的尾矿坝变形速率4级预警阈值确定方法更加科学合理,可用于尾矿库重要安全监测指标预警值的确定。     

考虑道路状况的应急配发站启用以及配送优化问题*

为了研究应急物资配发站启用及配送方案优化问题,综合考虑灾难事件下的道路损毁状况、道路复杂程度、需求不确定以及物资拆分配送等特点,以应急物资配发站启用成本、车辆启动成本、运输成本之和最小化为目标,建立应急物资配发站启用及配送模型,设计头脑风暴优化算法,结合算例,对该问题进行仿真,并将结果与遗传算法进行对比,验证模型的可行性和算法的有效性。结果表明,在决策时考虑道路状况能够有效降低系统成本,提高救援效率。     

Environmental Impact Assessment and Environmental Audit in Large-Scale Public Infrastructure Construction: The Case of the Qinghai–Tibet Railway

Large-scale public infrastructure projects have featured in China’s modernization course since the early 1980s. During the early stages of China’s rapid economic development, public attention focused on the economic and social impact of high-profile construction projects. In recent years, however, we have seen a shift in public concern toward the environmental and ecological effects of such projects, and today governments are required to provide valid environmental impact assessments prior to allowing large-scale construction. The official requirement for the monitoring of environmental conditions has led to an increased number of debates in recent years regarding the effectiveness of Environmental Impact Assessments (EIAs) and Governmental Environmental Audits (GEAs) as environmental safeguards in instances of large-scale construction. Although EIA and GEA are conducted by different institutions and have different goals and enforcement potential, these two practices can be closely related in terms of methodology. This article cites the construction of the Qinghai–Tibet Railway as an instance in which EIA and GEA offer complementary approaches to environmental impact management. This study concludes that the GEA approach can serve as an effective follow-up to the EIA and establishes that the EIA lays a base for conducting future GEAs. The relationship that emerges through a study of the Railway’s construction calls for more deliberate institutional arrangements and cooperation if the two practices are to be used in concert to optimal effect.     

我国清洁生产审核咨询机构现状分析及政策建议

对我国清洁生产审核咨询机构的主要功能、地位及其在政府与企业之间所发挥的联接作用进行了系统阐述.分析了我国清洁生产审核咨询机构的现状,总结了现阶段我国清洁生产审核咨询机构的主要特点及存在问题,并结合我国实际情况提出了发展我国清洁生产审核咨询机构的政策建议.     

Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN’s structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (μ) that represents the level of aspiration achieved in one objective and the conjugate of μ, (1 − μ), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN⁻, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying μ (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN⁻ and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.