基于SWMM模型的合流制管道溢流污染控制系统模拟

针对合流制管网污水溢流所导致的城市内涝和城市内河黑臭问题,以武汉市巡司河片区为例,利用暴雨洪水管理模型(storm water management model,SWMM)软件,构建该地区合流制排水系统的水力水质模型,模拟了不同降雨强度的暴雨模拟和全年降雨状况,分析了该地区溢流污染特征,据此提出了切实有效的合流制排水系统改造方案,并采用SWMM模型对不同改造方案的溢流水量和水质控制效果进行了模拟。结果表明,在箱涵与雨水调蓄池的共同作用下,对溢流污水量的截流比例在90%以上,年溢流次数小于10次,年溢流水量平均减少了1.75×10⁷ m³,对污染物的截流比例均在95%以上,每年溢流进入地表水中的COD减少了3 295.5 t,NH₃-N减少了505.8 t,TP减少了63.1 t,SS减少了1 981.1 t,溢流问题得到了极大的缓解,溢流污染能够得到较好的控制。随着暴雨重现期延长,截流效果会随之下降。箱涵和雨水调蓄池的组合措施对溢流污水具有明显的削减和调控作用,能够有效降低城市内涝隐患和缓解内河黑臭化问题,通过定量表征箱涵-雨水调蓄池组合在溢流污染方面的控制效果,为国内合流制排水管网改造提供技术支持和海绵城市设计提供参考。     

基于支持向量回归机的南黄海浒苔分布面积预测模型

绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息.     

基于改进灰色模型的管壁蜡沉积厚度预测研究

为对含蜡原油管道中的蜡沉积厚度进行准确预测,在函数cot(x2)变换的基础上,结合平移变换思想,利用cot(x2+c)变换建立新的改进GM(1,1)模型。以现场管道结蜡数据和室内环道结蜡数据为例,对比改进GM(1,1)模型、基于函数cot(x2)变换建立的GM(1,1)模型及传统GM(1,1)模型之间的预测精度,并分析平移量c对改进GM(1,1)模型预测精度的影响。结果表明:改进GM(1,1)模型的预测精度最高,其次是基于函数cot(x2)变换建立的GM(1,1)模型,而传统GM(1,1)模型的预测精度最低;随着平移量的增大,改进GM(1,1)模型的平均相对预测误差呈现出先减小后增大的趋势,因此合理的平移量有助于模型精度的提高。应用改进GM(1,1)模型来预测管道蜡沉积厚度是可行的,该方法可为含蜡原油管道蜡沉积厚度的准确预测提供参考和借鉴。     

基于情景分析的生产安全事故应急物资的协调储备优化研究

为了研究应急物资的实物储备与协议企业生产能力储备相结合的储备模式协调性,降低企业自身应对突发生产安全事故的物资储备成本,建立多情景生产安全事故下的企业应急物资实物储备场所和协议储备企业选址的协调优化模型,利用Lingo软件进行求解,并以石油储罐火灾的应急物资储备为背景设计算例,验证相关模型及方法的有效性。结果表明:提出的企业应急物资储备体系,能够在降低应急物资储备成本的同时,保持日常应急救灾能力。     

基于缓冲区重采样的LSTM林火预测模型*

为提高林火风险预测精度,挖掘地图上隐含的空间信息、时间序列上隐含的长期趋势和循环波动,提出1种基于缓冲区重采样的长短期记忆（LSTM）林火预测模型,选取15个与林火相关的影响因素,以方差膨胀因子为评价指标对其进行多重共线性检验,方差膨胀因子大于10的因素具有共线性,并采用信息增益率验证筛选结果的合理性。考虑到火灾的空间聚集特性,采用缓冲区分析与过采样相结合方法减少样本不均衡现象的影响,最终得到176 732条样本。对12个影响因素和研究时间段的火点建立LSTM预测模型,对森林火灾发生风险进行预测。研究结果表明:基于缓冲区重采样的LSTM林火预测模型有效考虑时空上隐含的信息,预测模型准确率为87.06%,特异性为97.99%,敏感度为76.12%,阳性预测率为97.43%,阴性预测率为80.41%,ROC曲线与AUC值均优于随机森林（RF）和支持向量机（SVM）这2种基准算法。维尔克松秩和检验发现,本文提出的模型与基准算法结果具有显著性差异。研究结果可为提高林火风险预测精度提供参考。     

大型公园道路网络关键节点脆弱性评价

为更加科学有效地辨识景区道路网络中的客流关键节点,以节点脆弱性为度量指标,提出1种基于FIM模型的关键节点脆弱性评价方法.以某大型公园为例,首先通过ArcGIS软件将该公园的道路网络信息转换成可编译的文本信息,经过Java枚举可行路径,然后利用嵌入FIM算法的Lingo进行优化,得出网络节点的重要度.最后综合节点容量、...     

基于云模型的城轨信号系统安全保障能力评价方法

为准确评价城轨信号系统安全保障能力及影响因素,基于云理论对定性与定量因素的良好融合优点,构建城轨信号系统安全保障能力评价云模型及评价指标体系,采用组合赋权法对各指标赋予权重,并基于专家意见,利用正向云发生器生成标准云.结果表明:该云模型具有一定稳定性和可靠性,可对城轨信号系统安全保障能力进行有效测评,同时能够发现安全运...     

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

Introduction: Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes. Method: This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area. Results: The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.     

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.