Calibration of deterministic NWP forecasts and its impact on verification

Deterministic forecasts (as opposed to ensemble or probabilistic forecasts) issued by numerical weather prediction (NWP) models require post-processing. Such corrective procedure can be viewed as a form of calibration. It is well known that, based on different objective functions, e.g., minimizing the mean square error or the mean absolute error, the calibrated forecasts have different impacts on verification. In this regard, this paper investigates how a calibration directive can affect various aspects of forecast quality outlined in the Murphy–Winkler distribution-oriented verification framework. It is argued that the correlation coefficient is the best measure for the potential performance of NWP forecast verification when linear calibration is involved, because (1) it is not affected by the directive of linear calibration, (2) it can be used to compute the skill score of the linearly calibrated forecasts, and (3) it can avoid the potential deficiency of using squared error to rank forecasts. Since no single error metric can fully represent all aspects of forecast quality, forecasters need to understand the trade-offs between different calibration strategies. To echo the increasing need to bridge atmospheric sciences, renewable energy engineering, and power system engineering, as to move toward the grand goal of carbon neutrality, this paper first provides a brief introduction to solar forecasting, and then revolves its discussion around a solar forecasting case study, such that the readers of this journal can gain further understanding on the subject and thus potentially contribute to it.     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

红砂岩地区基坑开挖对周边建筑物的影响

本文通过对衢州地区的一个深基坑工程周边的学校食堂、宿舍的建筑物沉降的监测和分析,探讨了红砂岩基坑开挖对周边建筑物产生的影响。监测结果表明,基坑开挖会引起周边建筑物不同程度的沉降变形,且主要发生在基坑开挖前的打桩阶段和基坑开挖阶段。两栋建筑物的沉降累计值基本已超过20mm,且最大的沉降量已接近40mm,较大程度超过房屋沉降监测警戒值,说明开挖的施工方案有待于进一步优化,此结论对于红砂岩地区基坑工程施工具有重要的工程意义。     

对国内外砂土地震液化评价介绍及比较

本文通过应用国内外不同的勘察规范对同一个工程场地的地震液化等级划分的计算及评价介绍,得出了一个采用不同规范,可取得比较一致的地震砂土液化等级的结论。     

山西王家岭煤矿底板奥灰水突水危险性评价

我国是受水害最严重的国家,奥灰水突水是我国很多煤矿在安全生产中急待解决的问题。山西王家岭煤业集团公司王家岭煤矿就面临着开采9号煤层底板奥灰水突水威胁的安全问题。结合王家岭煤矿实际钻孔数据,运用突水系数法、多层叠加抗压强度比值系数法分别对9号煤层进行突水危险性预测,并进行相互对比,根据计算所得结果得出9号煤层底板易发生突水区。     

邻近营业线施工既有桥梁自动化监测及费用分析

通过邻近营业线施工中对既有线桥梁进行影响分析,建立三维有限元模型分析不同线间距和不同施工阶段情况下既有线桥梁位移变形情况,对既有线桥梁进行实时监测和数据分析,用于指导现场施工,同时对传统人工监测方法和自动化监测费用分析对比,为后续类似工程施工积累相应的施工经验.     

Assessment of land use compatibility and noise pollution at Imam Khomeini International Airport

This study aimed to investigate land use planning around airports, by employing Remote Sensing (RS) and Geographic Information Systems (GIS), in conjunction with an optimization algorithm using an Integrated Noise Model (INM) software, to establish the potential effects of aircraft noise at Imam Khomeini International Airport (IKIA) in Tehran. We also checked for land use compatibility with the noise levels around IKIA and the residents' reaction to the noise. The research was carried out in three stages: a) the establishment of Strategic Noise Map (SNM) scenarios of the airport operation in the years 2011, 2020 and 2030 using the INM software; b) the assessment of the results with emphasis on the study area land uses and application of RS and GIS and the exposure of residents at different levels of environmental noise; and c) the assessment of the intensity of aircraft noise annoyance at various times of day and night. The results indicated that developing IKIA together with the residential development will increase airport noise. Hence proper management and control of noise at IKIA is essential.     

“中国梦”实现过程的中国经济总量和人民生活水平预测

文章分析了“中国梦”实现的三个阶段中,中国经济总量和人民生活水平将会发生的变化。文章预测：按实际汇率换算,2021年中国经济总量将超越美国成为世界第一经济大国,此时中国的人民生活水平将达到当时的世界平均水平;2049年中国的人民生活水平将达到世界高收入国家90%的生活水平;2065年中国的人民生活水平将达到最发达国家的生活水平,进而全面实现几代中国人梦寐以求的中华民族的伟大复兴。     

电力供需实验室的开发与建设

国家电网公司电力供需实验室是"十一五"期间国家电网公司重点建设的八大实验室之一。电力实验室由数据信息平台、业务支撑平台和应用平台3层平台构成。三者既相互独立,又相互联系。数据信息平台是电力供需实验室的基础。应用平台是直接面向用户、实现电力供需分析预测以及实验等功能的窗口,业务支撑平台是连接数据信息平台和应用平台的桥梁。目前,电力供需实验室已经基本具备了电力市场分析预测功能以及实验功能,可以应用于年度电力市场分析预测工作中。