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。     

非色散红外CO2飞机火警探测技术研究

提出了基于非色散红外吸收原理(NDIR)实时监测环境中CO2气体浓度变化进行火警探测的方法。根据高空大气环境的特殊性,探讨了基于NDIR的CO2气体探测技术应用于飞机上所面临的低温低压难题。进行了基于NDIR的红外CO2飞机火警探测器的两种结构设计,提出了基于CO2气体浓度监测的红外CO2飞机火警探测器的主要参数要求,研究了基于CO2浓度变化速率与变化值的火灾报警算法。     

Arrhenius公式和环境作用动力学对压缩橡胶的精度比较

目的 精确描述压缩橡胶永久变形率的变化规律,准确评估其使用寿命。方法 应用环境作用动力学理论,建立压缩橡胶永久变形率的变化微分方程,求解微分方程得到压缩橡胶永久变形率变化规律的微分方程通解。再以氟硅橡胶为例,通过高温加速试验获取其在100、125、150、175、200℃下的试验数据,分别采用环境作用动力学通解和阿伦尼乌兹公式建立氟硅橡胶压缩永久变形率模型。最后,与3.5 a的自然环境试验数据进行对比。结果 应用环境作用动力学理论建立的氟硅橡胶压缩永久变形率模型与3.5 a自然环境试验数据的最大误差约为3.32%,而应用阿伦尼乌兹公式建立的氟硅橡胶压缩永久变形率模型的最大误差约为14%。结论 阿伦尼乌兹公式只适用于没有环境作用或固定环境作用下的物质特征变化,而环境作用动力学方程中,有明确的环境作用项σ,适用于复杂环境作用下物质特征变化规律的描述。     

融合多源时空数据的地下水硫酸盐预测模型

准确预测地下水SO₄ ^2-空间变化趋势对改善地下水质量、提高区域地下水管理水平具有重要意义.以2011、2014、2017和2020年叶尔羌河流域平原区土地覆盖数据、土壤参数数据、数字高程数据等多源时空数据和地下水pH值为特征变量,分析其与地下水SO₄ ^2-浓度的相关性,利用贝叶斯优化算法优化随机森林回归,建立BOA-RFR模型,并基于BOA-RFR模型对特征变量进行重要性分析,对模型预测精度进行评价,最后生成地下水SO₄ ^2-预测图.结果表明,pH值、地面高程（GE）和贡献区荒地（BAR）面积占比作为影响地下水水化学组分的重要参数,与地下水SO₄ ^2-浓度均呈现极显著负相关,对地下水SO₄ ^2-浓度预测的重要度均大于25 %;地统计插值方法作为空间分布预测建模的辅助手段,加入辅助样本后的BOA-RFR模型,地下水SO₄ ^2-浓度预测的R²均大于0.96,且多辅助样本构建模型的RMSE和MAE最大值较少样本模型的最小值分别降低了4.7 %和23.8 %;在地下水SO₄ ^2-浓度预测中,高SO₄ ^2-地下水向叶尔羌河流域平原区东北部富集,且面积呈扩张趋势.