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.     

B-P网络用于环境质量分类研究

用全国２５个湖泊的水质指标资料作为训练样本,应用人工神经Ｂ－Ｐ算法,分别建立了３参数和４参数的水质分类模型,并用所建立的模型对６个湖泊水质进行分类检验效果。结果表明Ｂ－Ｐ模型应用于环境质量分类是可行的,且由于Ｂ－Ｐ网络有学习,自适应的能力,因而与其它的环境质量分类法相比具有优越性。     

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。     

重大危险源大规模人群疏散决策模型研究

为在重大危险源应急救灾中给决策人员提供最优的人群疏散方案,提高抢险救灾能力,将疏散过程分为两个阶段——先将灾民疏散至临时疏散救援点,再根据灾民受伤的严重程度有选择地将其疏散至定点医院进行救治。综合考虑灾害对人群疏散造成的影响,将道路危险系数等参数加入目标函数中,将灾区按灾害程度赋予不同的优先疏散系数并将之反映在时间满意度上,以总疏散时间最小为目标函数,建立疏散模型,并应用遗传算法进行求解。最后,通过MATLAB进行仿真计算。研究结果表明:模型和算法给出的疏散策略是有效的。     

基于双层规划的危险品运输专用车道优化设计

为更有效地设置危险品运输专用车道,在降低运输风险的同时减少专用道对路权的占用,从路网整体出发,以总体出行成本最小、路网运输风险最小和专用道利用率最大为目标,构建双层规划模型;使用NSGA-2算法求解双层规划模型,结合算例网络进行分析,验证模型和算法的有效性.结果表明:模型可综合考虑路网风险和总体出行成本,得到有效的专用...     

基于虚拟力的危化品仓储堆垛定位校正算法

超宽带定位技术作为一种室内高精度定位技术是解决危化品仓库堆垛贮存状态安全监测的有效方法,但在仓库非视距环境中,存在定位精度不足的问题,无法满足堆垛“五距”安全的监管要求。针对此问题,从UWB信号的信道模型和定位精度影响因素出发,利用高斯滤波消除了参考点在定位过程中由于系统和环境带来的随机误差影响,建立了参考点误差矢量库,并提出了一种虚拟力校正算法,利用建立的矢量库来对待测标签进行矢量补偿,达到提高定位精度的目的。实验表明,该方法可以提高危化品仓储堆垛定位精度,适用于监测危化品仓储状态。     

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.     

基于元胞遗传算法的多目标应急资源配置

为解决不同灾情下多目标多周期灾后救援问题,减少受灾损失,对灾后应急资源配置进行研究。从物流成本和系统损失2个方面最小化救灾行动的成本和最大化有限救灾资源的分配,建立基于路况的多目标应急资源配置模型,将帕累托前沿和超体积作为元胞遗传算法的求解性能指标,开展元胞遗传算法与遗传算法对模型的求解对比实验。结果表明:元胞遗传算法能较好地求解多目标多周期应急资源配置模型,且求解性能比遗传算法更好;通过对模型的求解,可为决策者基于不同灾情下的应急决策提供参考。     

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。