煤与瓦斯突出预测技术研究现状及发展趋势

对矿井煤与瓦斯突出预测方法的研究现状及发展趋势进行了论述及分析。目前我国大量使用的是钻屑量S、钻孔瓦斯涌出初速度q、瓦斯解吸指标Δh2 、瓦斯放散指数ΔP等钻孔静态预测方法 ,这些方法花费大量的人力、物力和财力 ,而且准确性较低。而声发射和电磁辐射等方法是很有前途的预测方法。未来煤与瓦斯突出预测的发展趋势是 ,利用声发射监测技术对变形破裂剧烈区域进行定位 ,利用电磁辐射监测技术工作面非接触连续预测 ,再结合现有的环境监测系统监测的瓦斯动态涌出对煤与瓦斯突出现象进行准确预测     

交通事故预测分析

运用模糊图、灰色系统、非线性回归、随机过程和安全系统工程的理论与方法，进行交通事故预测分析。给出了交通事故预测分析的双向模糊图模型、灰色马尔可夫预测模型及非线性回归模型。模型运行结果可为交通事故的预防和交通安全管理提供科学依据     

1983年日本海中部大震前无震应变前兆的倍九天活动特征

介绍了倍九律的科学内函以及在地震预报实际应用中的几个主要方面；进而对１９８３年５月２６日日本海中部Ｍｓ＝７．７级大震前的无震应变前兆的倍九天特征进行了分析，同时探讨了倍九律在日本海地区预报强余震的可参考性。     

Application of ecological theory to determining recovery potential of disturbed lotic ecosystems: Research needs and priorities

This article summarizes the views of aquatic scientists who gathered to assess the ability of stream ecosystem theory to predict recovery from disturbance. Two views of disturbance were evident: a discrete removal of organisms vs an unusual deviation from normal. These were perceived as applying to different scales and/or objectives. Long-term information is required from both points of view to define recovery. Recovery also may be defined in different ways, but it is clear that recovery has both spatial and temporal components, and includes both physical and biological processes. Consensus was very strong that a major role (and challenge) for theory lies in the understanding of spatial aspects, temporal scales, coupling of physics and biology, and the interaction of these features in recovery processes. Some progress is evident in the articles of this volume, but among the topics identified as critical for further theoretical contributions were: homogeneous vs heterogeneous distribution of disturbance, local extent of disturbance relative to a regional context, critical vs noncritical patches (size and location) of disturbance at different spatial scales and temporal frequencies, delineation of reversible and nonreversible processes, and physical and biological constraints on the time frame for recovery. Such concepts need attention across different types of lotic ecosystems. Thus, there was strong consensus that a national monitoring system of representative lotic ecosystems within ecological regions be established. The purpose of this monitoring system would be to acquire long-term data on natural variability, to establish viable indicators of spatial and temporal aspects of recovery, and to develop and test emerging theoretical developments.     

An online air pollution forecasting system using neural networks

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.     

雾霾天气预测中支持向量机的应用分析

支持向量机在对非线性复杂问题进行处理的过程中,展现出来的优势特征非常突出,本文针对雾霾天气预测中支持向量机的应用做出了进一步探究,对支持向量机的概念、支持向量机的基本思想、建立雾霾预测模型、预测试验给出了详细的分析。     

天津市煤气管网的震害预测及可靠性分析

本文研究了地下管道系统的震害预测及网络系统可靠性分析的方法。它是以地下管道微观破坏机制为根据,以模糊图论为主要计算工具的计算方法。最后,本文给出了天津市煤气管网系统的应用实例,结果表明,本方法有一定的推广应用价值。     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

三维空气质量模型初始场VOCs同化对臭氧预报的影响

于2023年2月15日—3月8日,采用中尺度数值预报模式/嵌套网格空气质量模式系统（WRF/NAQPMS）,分析了初始场同化6项常规大气污染物及挥发性有机物（VOCs）对广东省臭氧（O₃）预报的改进效果。 结果表明,同化6项常规污染物可显著降低O₃预报的标准化平均偏差（NMB）和均方根误差（RMSE）,NMB从-26%改善为-8%,RMSE从50.6μg/m³下降到35.0μg/m³。但对相关系数（r）的改善效果不佳,从0.51下降到0.49。相比于只同化常规6项污染物,同时同化VOCs对O₃的预报效果改善较为明显,r从0.49提高到0.63。此外,对NMB和RMSE的改善效果也较好,NMB从-8%改善为-3%,RMSE从35.0μg/m³下降到30.1μg/m³。相比于不同化,同化6项常规污染物的改善效果显著,空气质量指数（AQI）等级预报准确率可提升10%以上,AQI范围预报准确率可提升40%以上。相比于仅同化6项常规污染物,再增加同化VOCs,AQI等级预报准确率和范围预报准确率均提升5%左右,改善程度不高。     

灰场包气带环境影响预测方法研究——以新疆某电厂灰场为例

灰场在防渗不当或事故工况下,淋滤液的渗漏可能会对灰场包气带产生影响。以新疆某火电厂灰场的淋滤液渗漏情景为例,介绍了淋滤液渗漏对包气带影响的3种预测方法并进行对比分析。结果表明,两类解析法计算误差较大且存在各自适用条件,应优先采用数值法进行定量化预测。