基于GF-LSTM和GAN网络的小样本集人工水体溶解氧浓度预测

采用推流方式改善人工水体溶解氧分布不均衡以防止富营养化时,需要对其分布进行预测来提高推流效率,为此构建了基于生成式对抗网络(GAN,Generative Adversarial Networks)和长短期记忆神经网络(LSTM,Long-Short Term Memory Network)的溶解氧浓度预测模型。以广西大学镜湖35 m2的一片水体区域为研究对象,采用不同电压直流水泵推流,用无人船搭载在线检测仪在一段时间内定时定点采集水体溶解氧浓度数据作为原始数据样本,并采用GAN扩充数据样本。利用遗传算法和改进的一阶滤波算法进行溶解氧的噪声数据处理,结合LSTM网络构建溶解氧浓度预测模型GF-LSTM(Genetic And Filtering Algorithm-Long Short Term Memory Network)。结果表明:相比常用的BP网络,GF-LSTM网络预测的平均误差降低了62%,均方误差降低了75%;相比传统的LSTM网络,GF-LSTM网络预测的平均误差降低了22%,均方误差降低了50%。     

煤体电荷感应信号评价冲击危险性试验研究

为完善冲击地压矿井的冲击危险性评价方法,提高冲击危险性预测的准确率,应用自主研制的煤岩电荷监测系统,选择典型冲击地压矿井的煤样,开展了煤体单轴压缩冲击危险性测试与电荷感应监测试验研究。基于冲击地压扰动响应失稳理论,将应变软化阶段产生的电荷信号变化作为预测冲击地压发生的前兆信息,得到了煤体冲击危险性指标的临界软化系数Kρ、临界应力系数Kp及其冲击危险的等级分类标准,分析了煤体电荷感应信号的电荷事件数CSJ和电荷事件的平均幅值CFZ参量与冲击危险性指标Kρ和Kp之间的量化关系。结果表明:煤样破裂应力峰后,冲击倾向性K与电荷事件数CSJ呈指数递减关系,与CFZ呈指数递增关系;随着Kρ或Kp的增大,煤样应力峰后CSJ呈幂函数关系递增,应力峰后电荷平均幅值CFZ呈一次函数关系递减,以此可预测煤体的冲击危险程度,最后得到了河南某矿煤层冲击危险性的应力峰后冲击危险等级的CSJ和CFZ判据与划分标准。煤体冲击危险性电荷感应信号评价方法为冲击地压矿井冲击危险性评价提供了一种新的方向,对现场煤层冲击危险性评价具有指导作用,但也还需要开展大量现场试验对其进行不断修正和完善。     

Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.     

不同噪声强度对煤矿工人作业失误率的影响研究

为探究噪声强度对煤矿工人作业失误率的影响程度,采用实验室模拟试验和现场调研收集数据的方式对噪声和作业失误率间的关系展开研究,利用模拟试验所得结果和煤矿现场调研收集结果分别构建噪声-作业失误率、噪声-"三违"数量回归预测模型,定量分析作业失误率、"三违"数量和噪声水平之间的关系,研究结果表明:煤矿"三违"数量、实验室模拟人员失误率与环境噪声均呈正相关,随噪声强度增加,失误率及"三违"数量呈上升趋势;当外界噪声强度高于75 dB后,人员失误次数会出现显著性变化;基于事故预防的角度出发,可将75 dB设定为噪声安全临界值,以降低人员作业失误率,达到事故预防的目的。     

Effects of different management strategies on long-term trends of Australian threatened and near-threatened mammals

Monitoring is critical to assess management effectiveness, but broadscale systematic assessments of monitoring to evaluate and improve recovery efforts are lacking. We compiled 1808 time series from 71 threatened and near-threatened terrestrial and volant mammal species and subspecies in Australia (48% of all threatened mammal taxa) to compare relative trends of populations subject to different management strategies. We adapted the Living Planet Index to develop the Threatened Species Index for Australian Mammals and track aggregate trends for all sampled threatened mammal populations and for small (<35 g), medium (35–5500 g), and large mammals (>5500 g) from 2000 to 2017. Unmanaged populations (42 taxa) declined by 63% on average; unmanaged small mammals exhibited the greatest declines (96%). Populations of 17 taxa in havens (islands and fenced areas that excluded or eliminated introduced red foxes [Vulpes vulpes] and domestic cats [Felis catus]) increased by 680%. Outside havens, populations undergoing sustained predator baiting initially declined by 75% but subsequently increased to 47% of their abundance in 2000. At sites where predators were not excluded or baited but other actions (e.g., fire management, introduced herbivore control) occurred, populations of small and medium mammals declined faster, but large mammals declined more slowly, than unmanaged populations. Only 13% of taxa had data for both unmanaged and managed populations; index comparisons for this subset showed that taxa with populations increasing inside havens declined outside havens but taxa with populations subject to predator baiting outside havens declined more slowly than populations with no management and then increased, whereas unmanaged populations continued to decline. More comprehensive and improved monitoring (particularly encompassing poorly represented management actions and taxonomic groups like bats and small mammals) is required to understand whether and where management has worked. Improved implementation of management for threats other than predation is critical to recover Australia's threatened mammals.     

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

“十三五”期间湖南省资江流域锑污染时空分布及污染治理成效分析

为掌握“十三五”期间湖南省资江流域锑污染的总体情况和变化趋势,采用数据统计分析方法对2016—2020年该流域水质锑变化特征进行研究,同时分析锑污染时间变化趋势和空间分布情况。结果表明,“十三五”期间,资江流域锑污染整体有较明显改善,尤其资江干流下游益阳市内的锑污染明显减轻,由2016年的超标率100%到2020年全部实现达标,各断面锑的质量浓度最大降幅达55.5%。资江支流中,2020年邵水断面锑质量浓度年均值较2018年下降了13.1%。资江流域的娄底、邵阳和益阳市均采取了一系列锑污染防治措施,使得资江流域锑污染整体有较明显改善。但资江支流中邵水和平溪江污染最重断面锑质量浓度年均值仍有超标,建议“十四五”期间加强流域内涉锑企业和历史遗留污染的治理,力争尽早实现资江全流域水质的全面达标。     

震后泥石流治理工程有效性评价及存在问题分析

汶川8.0级地震及芦山7.0级地震后,针对严重的泥石流灾害,在强震区实施了大量防治工程.通过对地震重灾区92条泥石流沟治理工程实地调查分析,初步建立了基于事前(P)、事中(N)、事后(F)阶段的泥石流防治工程效果评价体系,并利用该评价体系对研究区的泥石流防治工程进行了有效性评价,治理工程效果"良好-优"的占到总数的59...     

Modeling using dynamic variables – An approach for the design of loss prevention programs

Records from The National Safety Council [National Safety Council, 2007. Safety Intervention Evaluation: A Systematic Approach. <http://www.acgih.org/events/ControlBand/Thomas_SafetyIntervention.pdf> (accessed 10.01.07)] have shown that in 2004 alone, on-the-job injuries to workers constituted 35% of total recorded injuries in the United States. This generated an associated cost of about $142.2 billion. Unfortunately, the safety intervention programs enforced at work places to mitigate such losses are driven mainly by intuition and experience of involved safety personnel. This paper details implementing a computer program to furnish safety personnel with an empirical basis for designing loss prevention programs based on historical safety data. The computer tool is driven by a dynamic mathematical model which adapts itself to variations in data patterns and explains the correlation between historical incident rates and corresponding resources committed to interventions. This study empowers the industry with a tool that is capable of forming the core of optimizing valuable human resource allocation in safety program designs.     

Fisher判别法在煤与瓦斯突出危险程度预测中的应用

为了提高煤与瓦斯突出的预测精度,根据煤与瓦斯突出的综合作用假说,选取开采深度、瓦斯压力、瓦斯放散初速度、煤的普氏系数以及煤体破坏类型作为判别指标。利用国内典型突出矿井20个实测数据作为训练样本,建立煤与瓦斯突出危险程度预测的Fisher判别分析模型,并应用于其他待判样本的预测。结果表明:Fisher判别分析模型能够反映多因素对煤与瓦斯突出的影响,分类性能良好,误判率低,借助SPSS软件实现,具有计算简单的特点,是煤与瓦斯突出预测的一种有效方法。