Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

Performance evaluation of NOAA-EPA developmental aerosol forecasts

To aid air quality model development and assess air quality forecasts, the Meteorological Development Laboratory (MDL) provided categorical verification metrics for developmental aerosol predictions. The National Air Quality Forecasting Capability (NAQFC) generated 48 h (of) gridded hourly developmental predictions for the lower 48 states (CONUS) domain in 12 km horizontal spacing. The NAQFC uses the North American Mesoscale (NAM) model with EPA’s Community Multiscale Air Quality (CMAQ) model to produce predictions of ground level aerosol concentrations. We used bilinear interpolation to calculate predicted daily maximum values at the location of the observation sites. We compared these interpolated predicted values to the observed daily maximum to produce 2 × 2 contingency tables, with a threshold of 40 μg/m³ during the months of March–August, 2007. The model showed some degree of skill in predicting aerosol exceedances. These results are preliminary as the NAQFC model for aerosol prediction is in the developmental stage. A more comprehensive performance evaluation will be accomplished in 2008, when more data become available. Our verification metrics included categorical analyses for Fraction Correct (FC) or percent correct (FC × 100), Threat Score (TS) or Critical Success Index (CSI), Probability of Detection (POD), and the False Alarm Rate (FAR), Mean Absolute Error (MAE) and mean algebraic error or bias, where bias is forecast minus observation. Graphic products included weekly statistics for the CONUS displayed in the form of bar charts, scatterplots, and graphs. In addition, we split the CONUS into six geographic regions and provided regional statistics on a monthly basis. MDL produced spatial maps of daily 1-h maximum predicted aerosol values overlaid with the corresponding point observations. MDL also provided spatial maps of the daily maximum of the 24-h running average. We derived the 24-h running average from the 1-h average predicted aerosol values and observations.     

PDE accident model from a safety information perspective and its application to Zhangjiakou fire and explosion accident

Accident models can provide theoretical frameworks for determining the causes and mechanisms of accidents, and thus are theoretical bases for accident analysis and prevention. The role of safety information in accident causation is profound. Thus, safety information is an important and essential perspective for developing accident models. This study presents a new accident model developed from a safety information perspective, called the Prediction—Decision—Execution (PDE) accident model. Because the PDE accident model is an emerging accident model that was proposed in 2018, its analysis logic and viability remain to be discussed. Thus, the main contributions of this study include two aspects: (i) detailed explanation of the analysis logic of the PDE accident model, and (ii) case-study examination of the Zhangjiakou fire and explosion accident, a serious accident that occurred in China in 2018, to demonstrate the viability of the PDE accident model. Results show that this is a safety-information-driven accident model that can provide a new and effective methodology for accident analysis and prevention, and safety management.     

基于粗糙集和BP神经网络的流域水资源脆弱性预测研究——以淮河流域为例

水资源是一种重要的自然资源和经济资源,对其未来的脆弱性进行预测可以预估研究区未来的水安全状况,对其脆弱性问题做出预警,从而及时采取治理措施。因此,合理科学的水资源脆弱性预测研究是缓解水资源脆弱性的有效手段。目前,水资源脆弱性研究主要是针对水资源现状进行评价,对其未来状况的预测较少。集成了粗糙集和BP神经网络两种方法,首先采用改进了的盲目删除法对构建的流域水资源脆弱性评价指标体系进行约简,其次通过BP神经网络拟合约简后的指标数据与脆弱度之间的映射关系,构建流域水资源脆弱性评价预测模型。基于之前研究的样本数据和脆弱性结果,探讨淮河流域未来的水资源脆弱性状况。结果表明:淮河流域2015年、2020年和2025年的水资源脆弱度分别为0.305、0.359和0.390,处于轻度脆弱和中度脆弱的状况,除2015年脆弱性状况有所好转以外,2020年和2025年的水资源脆弱性程度与近几年相比有所加剧,根据指标数据可知该现象主要是受年降水量、人均用水量、万元GDP废水排放量、垦殖指数、有效灌溉面积比和干旱面积受灾比6个指标的影响,为避免水资源脆弱性的加剧,应当有针对性的加强这几个方面的管理和控制。     

A classification and regression tree model of controls on dissolved inorganic nitrogen leaching from European forests

Often, there is a non-linear relationship between atmospheric dissolved inorganic nitrogen (DIN) input and DIN leaching that is poorly captured by existing models. We present the first application of the non-parametric classification and regression tree approach to evaluate the key environmental drivers controlling DIN leaching from European forests. DIN leaching was classified as low (<3), medium (3-15) or high (>15 kg N ha⁻¹ year⁻¹) at 215 sites across Europe. The analysis identified throughfall NO₃⁻ deposition, acid deposition, hydrology, soil type, the carbon content of the soil, and the legacy of historic N deposition as the dominant drivers of DIN leaching for these forests. Ninety four percent of sites were successfully classified into the appropriate leaching category. This approach shows promise for understanding complex ecosystem responses to a wide range of anthropogenic stressors as well as an improved method for identifying risk and targeting pollution mitigation strategies in forest ecosystems.     

人工神经网络在环境科学中的应用研究

以B-P网络为例介绍了人工神经网络模型的应用原理。以开拓人工神经网络在环境科学中的应用范围为目的,综述了几种常用神经网络模型在环境预测与评价2方面的应用性能。结果表明:人与环境关系的科学量度是多方位、多因素的非线性模糊问题,而人工神经网络模型具有解决该类问题的独特优点。针对不同环境问题,优选模型,开拓人工神经网络方法在环境科学中的应用范围,为环境问题的解决提供新的技术手段。     

Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake

Algal blooming has become one of the key fields of study on eutrophication of water body recently. The mechanism of algal blooming is still not understood well. However, it is obvious to understand that algal blooming has close relationship with chlorophyll-a. Therefore, if the trends of chlorophyll-a concentration can be simulated accurately, it will be helpful for the prediction of algal bloom events. In this study, a model named Environmental Fluid Dynamics Code (EFDC), which was developed by U.S. Environmental Protection Agency, was described and used to simulate the eutrophication process in the Daoxiang Lake, Beijing. To run the eutrophication model for the Lake, a field sampling was conducted in March-October of 2008 with interval of 10-20 days. Meanwhile, the algal bloom assessment criteria were investigated and the indicator of chlorophyll-a concentration was selected as input for the prediction of algal bloom in the Daoxiang Lake. After model calibration and validation, traditional statistics has been done between modeled results and observed values. The modeled results show that the simulated chlorophyll-a concentration basically agrees with the observed concentration except the later period of station 2# and the average algal bloom prediction accuracy is 63.43%. It was verified that the EFDC model can be used for chlorophyll-a concentration simulation and algal blooming prediction in the Daoxiang Lake.     

基于MIKE11与EFDC模型的突发性水污染事故预测模拟研究

通过对沈阳市浑河流域水环境的调查,基于Mike11与EFDC建立浑河流域沈阳段水动力-水质耦合模型,并对突发污染事故进行模拟研究。结果表明,Mike11与EFDC模型应用于浑河流域的预警模型在率定期和验证期的模拟结果均良好,所确定的参数能准确反映河道特征。因此,此预警模型可应用于浑河流域突发性水环境风险应急预测,并且能够对浑河污染物迁移转化过程进行准确模拟预测,模拟结果能够为区域水资源管理和保护提供技术支持,达到流域应急预警功能需求。