A TREATISE ON THE USE OF EXPECTED VALUES IN WATER RESOURCE PROJECT EVALUATION1

This paper focuses on two factors that influence the acceptability of the expected value criterion as a method of handling risk or uncertainty in decision-making where damages from unusual physical occurrences such as hurricanes or floods must be considered. These are the length of time of project existence and the problem of obtaining accurate estimates of the probability of occurrence for different sizes of natural disasters. The problem of estimating accurate probabilities of occurrence for different sizes of natural disasters is not as great as it might appear and in most situations will not defeat the usefulness of the method. The time span during which the project is expected to exist has greater implications for the usefulness of the expected value approach appears highly applicable. However, it becomes less applicable for projects that are short-term in nature. The basis for distinguishing between short- and long-term projects is presented.     

EVIDENCE OF CHAOTIC BEHAVIOR IN SINGAPORE RAINFALL1

ABSTRACT: This paper focuses on the investigation of the existence of chaotic behavior in the Singapore rainfall data. The procedure for the determination of the minimum number of variables essential and the number of variables sufficient to model the dynamics of the rainfall process was studied. An analysis of the rainfall behavior of different time periods was also conducted. The correlation dimension was used as a basis for discriminating stochastic and chaotic behaviors. Daily rainfall records for durations of 30, 20, 10, 5, 4, 3, 2, and 1 years from six stations were analyzed. The delay time for the phase-space reconstruction was computed using the autocorrelation function approach. The results provide positive evidence of the existence of chaotic behavior in the daily rainfall data. The minimum number of variables essential to model the dynamics of the rainfall process was identified to be 3 while the number of variables sufficient to model the dynamics of the rainfall process ranges from 11 to 18. The results also suggest that the attractor dimensions of rainfall data of longer time periods are higher than that of shorter time periods. The study suggests a minimum number of 1500 data points required for the computation of the correlation dimension of the rainfall data.     

哈尔滨市短临天气预报研究

从短临预报的定义和内容出发，就短临预报业务系统流程、地面要素加密信息收集显示、多普勒雷达预警信息和预报指标、重大灾害性短临预报的气象要素信息／预警信息／预报指标及物理量、不稳定指数预警信息／预报指标及合并显示、卫星云图降水／冰雹估计、3h降水／温度预报方法、预报编辑系统设计等方面，阐述了哈尔滨市短临预报业务的建设过程及其开展的方式、方法。     

基于人体健康风险的水污染事件遗传性致癌物安全浓度研究

确定水污染事件中遗传性致癌物短期暴露安全浓度,是目前我国水环境管理中急需解决的问题.在假设遗传性致癌物暴露剂量与致癌风险呈线性相关的前提下,提出了水体中遗传性致癌物安全浓度计算流程,建立了采用遗传性致癌物终生暴露安全浓度计算短期暴露安全浓度的方法.在我国2000～2010年间发生的60起水污染事故中,特征污染物为砷的水污染事件为6起,占总污染事件比例为10%,这一结果表明砷为我国水污染事件中高发的特征污染物.根据水污染事件遗传性致癌物短期暴露安全浓度计算方法,得出砷短期暴露安全浓度为0.5 mg.L-1,表明该方法在水污染事件应急管理中具有一定的可行性.     

基于CNN_LSTM模型的复杂支路故障电弧检测*

在低压交流配电系统中,当多支路并联的复杂系统的某1支路中出现串联电弧故障时,识别难度大幅提升。为了预防此类情况引发的电气火灾,提出1种卷积神经网络(CNN)与长短时记忆网络(LSTM)结合的串联故障电弧检测方法。首先,搭建实验平台用以采集不同负载在不同支路下发生故障时和正常工作时的干路电流数据;然后,构建CNN_LSTM模型并做出相应改进,将电流数据直接输入到模型中,由模型自主提取波形特征并进行分类。研究结果表明:该方法可以快速、准确地识别出电弧故障,准确率达99.04%以上,且能够较为准确地检测出是哪类负载所在的支路发生电弧故障,准确率达97.90%,可为复杂支路下的电弧故障识别研究提供参考。     

生活垃圾填埋短期好氧预处理工艺优化研究

以模拟有机生活垃圾为底物,通过实验研究了通风和温度协同控制作用下,填埋短期好氧预处理过程垃圾VS、含水率和有机组分的代谢情况.结果表明,好氧预处理过程VS降解主要发生在反应前期(0~6d),占实验全过程降解量的68%~85%;同时垃圾含水量的15%~26%以渗滤液和气体的形式被去除,并且延长通风时间能够增加后者所占比例.反应前期堆体自发升温到高温状态(50~55℃)比控制堆体温度维持在中温状态(42~47℃)更利于碳水化合物的代谢,但对粗蛋白降解的影响不明显.而在中温状态下粗脂肪和木质纤维素的降解率更高.继续延长预处理时间至14d,不同通风和温度的组合对VS和水分总体去除差距不大.控制填埋好氧预处理周期在6d并尽量维持堆体温度在50~55℃,成本-效益较高.     

济南市大气颗粒物短期暴露对老年人群血清淀粉样蛋白P组分影响的定群研究

为了解济南市不同粒径大气颗粒物暴露对老年人群血清淀粉样蛋白P组分（SAP）的急性影响,评估大气颗粒物暴露对人群神经系统的健康风险,以山东省济南市甸柳社区为研究地点,采用定群研究设计,对社区76名健康老年人进行5次重复测量,结合社区附近（< 2 km）环保监测超级站的PM_2.5和PM₁₀浓度数据,在控制年龄、性别等协变量情况下,利用线性混合效应模型分别分析PM_2.5和PM₁₀暴露对SAP的效应影响.结果表明:大气颗粒物短期暴露与老年人群SAP的升高呈正相关;PM_2.5累积滞后0~6 h,每升高1个四分位数间距（IQR）,SAP水平升高18.73%（95% CI为9.20%~29.08%）（FDR_H-P < 0.05）（其中,CI为可信区间,FDR_H-P为经多重校正后的P值）;PM₁₀累积滞后0~6 h,每升高1个IQR,SAP水平亦升高,但变化不显著（FDR_H-P>0.05）.研究显示,大气颗粒物暴露可以引起人群反应神经退行性病变的SAP指标升高,提示大气颗粒物对人群神经系统具有潜在威胁;PM_2.5对SAP的效应高于PM₁₀,提示小粒径颗粒物具有较高的健康危害,应加强对小粒径颗粒物的健康影响和干预防护研究.      

基于复合LSTM模型的PM2.5浓度预测

在对淄博市19个空气质量监测站点监测数据进行分析后,提出了一种基于机器学习的复合模型——灰色关联度分析(GRA)-改进的完备总体经验模态分解(ICEEMD)-长短期记忆网络(LSTM)模型。通过分析淄博市2019年大气污染物和气象数据,选用LSTM模型预测PM_2.5浓度。由于传统单一模块机器学习模型具有训练时间较长和预测精度较低的问题,提出了复合LSTM模型。该模型由3部分组成：GRA,用于PM_2.5浓度影响因素变量筛选;ICEEMD,用于PM_2.5分解、分量筛选和原始大气污染物及气象数据处理;LSTM,用于PM_2.5浓度预测。预测结果表明：淄博市中部丘陵地带PM_2.5浓度高于南部山区和北部平原,东部高于西部;淄博市逐月PM_2.5浓度呈“U”形分布,1月最高,8月最低;淄博市PM_2.5浓度受PM₁₀和CO影响较大,受湿度和温度影响较小。对比单一LSTM模型和GRA-LSTM模型,GRA-ICEEMD-LSTM模型...     

中国高铁建设投资对国民经济和环境的短期效应综合评估

近年来,传统铁路发展远不能满足各行业对综合交通运输体系的需求,中国政府正加大对高铁行业的规划与投资。现有高铁网络与其他铁路共同构成的快速客运网,推动了产业的空间布局,加速了产业结构调整,提高了就业率,提升了区域间劳动力、资本、自然资源等配置效率,促进了区域经济协调发展。在中国政府大力建设高铁网络的当下,综合评估中国高铁建设投资给国民经济和环境带来的影响具有必要性和十分重要的现实意义。本文基于中国2012年投入产出表,利用投入产出技术和计量经济学模型,通过重新刻画投入产出模型中农村居民、城镇居民部门"收入-消费"内生关系,区分高铁与传统铁路在生产结构和投资结构的差异,构建基于居民消费局部内生化的高铁投资投入产出局部闭模型,评估中国高铁建设投资对经济、就业和能源环境的短期效应。结果表明:12012年中国高铁投资每亿元拉动总产出增加3.72亿元,GDP增长1.21亿元,投资总量给国民经济共带来总产出增长19 373.44亿元、GDP增长6 296.04亿元;2在拉动经济增长同时,高铁建设投资对就业拉动显著,每亿元高铁投资创造1 084个就业岗位,2012年因高铁投资新增就业岗位高达565.23万人。这意味着,在短期内,中国高铁建设投资会在发展社会经济、促进居民就业等方面注入强大的动力;3在环境层面,2012年高铁前期基础建设共带来83.42 Mt CO2排放,略大于传统铁路投资,但考虑到建成运营后,高铁具有清洁、稳定、高速等特点,高铁建设投资在环境层面优势会慢慢显现。     

Short-Term Hydroscheduling with Discrepant Objectives Using Multi-Step Progressive Optimality Algorithm1

Cheng, Chuntian, Jianjian Shen, Xinyu Wu, and Kwok-wing Chau, 2012. Short-Term Hydroscheduling with Discrepant Objectives Using Multi-step Progressive Optimality Algorithm. Journal of the American Water Resources Association (JAWRA) 48(3): 464-479. DOI: 10.1111/j.1752-1688.2011.00628.x Abstract: With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term hydroscheduling (STHS) problem to improve the quality of optimal solutions and enhance the convergence speed of progressive optimality algorithm (POA). In MSPOA, the original problem is first decomposed into a sequence of problems with the longer time steps. Next, the problem with the longest time step is solved, and the optimal solution is used as the initial solution for the problem with the second longest time step. This process proceeds until the original problem with the shortest time step is solved. The proposed discrepant-objective method and solution technique are tested for two types of hydroelectric systems. The results show that MSPOA can give better solutions and cost less time than POA due to enlarging feasible range of decision variables and reducing the number of computational stages. Discrepant objectives among hydropower plants can express the operation characteristics of complex hydropower systems more accurately than unique objective or multiple objectives.