基于支持向量机的巢湖富营养化程度评价研究

由于湖泊富营养化程度影响因素多,评价因素与富营养化等级之间关系复杂而且具有非线性特征。支持向量机是由Vapnik等人提出的建立在统计学习理论基础上的一种新的机器学习方法,由于其使用结构风险最小化原则代替经验风险最小化原则,解决了一些神经网络遗留的问题,又由于其应用了核函数思想,它可以较好地解决非线性问题,利用支持向量机多类分类算法,构建巢湖富营养化程度评价模型,取得较好的结果。     

粉碎研磨设备粉尘爆炸的预防

粉碎研磨设备火灾爆炸事故发生率较高。针对该过程的火险特点,防火对策应从防止粉尘沉积,消除粉尘源,防止摩擦、撞击、生热,消除静电危害,搞好工艺防火,惰性介质保护,设置防爆泄压装置,粉尘爆炸的抑制,火灾事故处理措施,加强消防安全教育诸方面加以考虑。     

综采工作面人员操作可靠性与割煤机运行方式的选择与分析

应用人机工程及可靠性理论,分析了连续作业时间和割煤机运行方式对综采工作面人员操作可靠性的影响,建立了割煤机运行方式的优化模型。根据模型所选择的割煤机运行方式,既能提高工作面产量,也能提高人员操作可靠性,有利于综采工作面高效安全生产     

RS-PSO-ELM下腐蚀管道失效压力预测

为提高腐蚀管道失效压力的预测精度并简化其计算过程,提出基于粗糙集(RS)和粒子群算法(PSO)融合极限学习机(ELM)的腐蚀管道失效压力预测模型。通过属性约简提取影响失效压力的关键因素,选用PSO优化ELM的输入权值和隐含层偏差,将归一化的核心指标数据代入计算。结果表明:该模型预测结果与实际值基本一致,与单一ELM模型相比,预测结果的均方差(MSE)降至0.255;与其他蚀管道失效压力评价模型相比,该模型预测结果的绝对误差平均值降至0.32。     

Identifying socioeconomic and biophysical factors driving forest loss in protected areas

Protected areas (PAs) are a commonly used strategy to confront forest conversion and biodiversity loss. Although determining drivers of forest loss is central to conservation success, understanding of them is limited by conventional modeling assumptions. We used random forest regression to evaluate potential drivers of deforestation in PAs in Mexico, while accounting for nonlinear relationships and higher order interactions underlying deforestation processes. Socioeconomic drivers (e.g., road density, human population density) and underlying biophysical conditions (e.g., precipitation, distance to water, elevation, slope) were stronger predictors of forest loss than PA characteristics, such as age, type, and management effectiveness. Within PA characteristics, variables reflecting collaborative and equitable management and PA size were the strongest predictors of forest loss, albeit with less explanatory power than socioeconomic and biophysical variables. In contrast to previously used methods, which typically have been based on the assumption of linear relationships, we found that the associations between most predictors and forest loss are nonlinear. Our results can inform decisions on the allocation of PA resources by strengthening management in PAs with the highest risk of deforestation and help preemptively protect key biodiversity areas that may be vulnerable to deforestation in the future.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

地铁沿线老旧房屋结构安全预警模型

为研究城市地铁沿线老旧房屋普遍存在结构安全问题,基于机器学习模型,选取房屋年份、楼层、面积等11个属性构建预警指标体系,采用SMOTE过采样、独热编码等方法解决样本离散化、不均衡的问题;利用KNN、Bayes、Logistic、SVM 4种机器学习模型对房屋结构安全数据学习并测试,综合应用Accuracy、F1、AP、...     

桥梁建设的安全重心——架桥机安全运行探讨

对铁路、公路桥梁建设中使用的关键设备——架桥机事故及安全隐患进行统计、总结,结合架桥机的结构特点、现场环境、使用状况进行安全使用管理分析。阐明对结构复杂、作业环境差、操作繁琐的机电液一体化的大型设备——架桥机现场操作安全管理的核心是各操作步骤中对安全状况的检查确认;在架桥机安全使用条件、操作人员、安全检查、安全试验等方面提出要求,对架桥机吊梁、过孔、变跨、收尾、维护保养等作业过程的安全操作进行规范,指出对架桥机现场施工科学安全监督管理的内容,全面、系统地完善架桥机安全使用管理体系。     

混合液体火灾爆炸危险性——闪点预测与实验研究

支持向量机应用核函数技术,已经成为当前国际上一个研究的热点,由于支持向量机具有良好的理论基础和泛化性能,可将其引入到混合液体闪点预测的研究之中,以期建立准确、高效的预测模型。本文建立了一个基于支持向量机的理论模型,用于预测二元互溶混合液体的闪点。根据所研究混合液体的物理性质,选择了纯物质的粘度、表面张力、配比、燃烧下限等物理参数来表征闪点,以这些参数作为输入参数,二元混合液体的闪点作为输出值,应用支持向量机方法对两者之间的内在定量关系进行模拟。结果表明,闪点预测值与实验值符合良好。本方法的提出为工程上提出了一种预测二元互溶液体闪点的有效方法,可应用于评估混合溶液的火灾爆炸危害性及本质较安全设计。     

工程机械不同工况下PM_(2.5)排放及其碳质组分特征

我国工程机械排放控制起步较晚.为研究实际工况下工程机械的PM2.5排放特性及其碳质组分构成,采用便携式颗粒物稀释采样系统,对3台工程机械(2台挖掘机和1台装载机)在不同典型工况(行驶、作业和怠速)下的PM2.5及其碳质组分〔OC(有机碳)和EC(元素碳)〕的现场排放特征进行了测试.结果表明:沃尔沃挖掘机、山河智能挖掘机的PM2.5排放因子(基于燃油)分别为1.85~3.26和1.56~2.62 g/kg,厦工装载机的PM2.5排放因子为0.98~1.48 g/kg.不同工况对PM2.5排放因子影响较大,怠速工况下PM2.5排放因子是行驶工况下的1.49~1.76倍.工程机械排放的PM2.5中,碳质组分是最主要的成分,其质量分数高达71.0%~84.5%.其中,w(OC)为44.6%~72.0%,在怠速工况下最高;w(EC)则为8.6%~30.9%,在行驶工况下较高.测试工程机械的PM2.5排放水平较高,因此应尽快加强工程机械排放的污染防治.