Computerised Decision Support Systems for the management of freshwater radioecological emergencies: assessment of the state-of-the-art with respect to the experiences and needs of end-users

Assessment of the environmental and radiological consequences of a nuclear accident requires the management of a great deal of data and information as well as the use of predictive models. Computerised Decision Support Systems (CDSS) are essential tools for this kind of complex assessment and for assisting experts with a rational decision process. The present work focuses on the assessment of the main features of selected state-of-the-art CDSS for off-site management of freshwater ecosystems contaminated by radionuclides. This study involved both developers and end-users of the assessed CDSS and was based on practical customisation exercises, installation and application of the decision systems. Potential end-users can benefit from the availability of several ready-to-use CDSS that allow one to run different kinds of models aimed at predicting the behaviour of radionuclides in aquatic ecosystems, evaluating doses to humans, assessing the effectiveness of different kinds of environmental management interventions and ranking these interventions, accounting for their social, economic and environmental impacts. As a result of the present assessment, the importance of CDSS “integration” became apparent: in many circumstances, different CDSS can be used as complementary tools for the decision-making process. The results of this assessment can also be useful for the future development and improvement of the CDSS.     

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.     

Explosion at an aluminum factory caused by the July 2018 Japan floods: Investigation of damages and evacuation activities

An explosion at an aluminum factory occurred due to floods in Soja City, Okayama Prefecture, in July 2018. This study investigates the accident, its overall impact on local residents, and emergency management activities. The results of the study showed that several nearby structures and homes were damaged by the explosion. Concern for more explosions has led to the evacuation of residents of Shimobara area of Soja City. In Shimobara, residents had carried out voluntary disaster prevention activities before the disaster. After the explosion, Shimobara residents were able to evacuate safely in cooperation with the voluntary disaster prevention organization and the local government agency. The study revealed that there was a lack of information on chemical accident hazards shared by the factory with local government authorities and first responders, as well as with local residents. In order to be better prepared for technological accidents, and in particular accidents caused by natural disasters (known as Natechs), mechanisms for information sharing between companies and municipalities should be adopted.     

洪水预报调度决策支持系统的设计与开发

基于C/S和B/S相结合的结构模式,开发了九龙江流域洪水预报调度决策支持系统。该系统采用了马斯京根矩阵解法、卡尔曼滤波实时校正、交互式调度方案生成、ASP等关键技术,实现了信息管理、洪水预报、洪水调度和系统管理等功能。     

拱形支架在煤矿巷道中的应用研究

针对目前一些高压力和不宜采用锚杆支护的巷道,为适应其支护的要求,研制开发了拱形支架。对于巷道支护,其着眼点应放在充分利用和发挥岩层的自承能力上。对本文的拱形支架支护,根据力法原理,采用曲梁模型进行了支架系统的力学分析,得出其最大承载能力;通过与直梁支架承载能力相比较,得到了拱形支架承载能力系数与支架半径和巷道跨度的影响关系。同时为了使巷道断面更为合理,对支架构件轮廓曲线进行了优化,以使支架构件的内力和变形最为合理。结果证明：拱形支架承载能力比直梁梯形支架的承载能力有大幅度提高,其半径和跨度直接影响着承载能力,从而有效解决了软岩巷道难以支护的问题。另外,从合理拱轴线的角度,得出在均布等压的巷道中,选用圆弧形支架构件进行支护,能最大程度地发挥支架的力学性能。     

基于优化算法的弹药贮存寿命预测方法

目的 实现对缺失及不足的制导弹药贮存失效数据预测及补充的能力。方法 首先通过4种不同的预测算法（GA-BP、PSO-BP、GA-SVM、PSO-SVM）,对自然贮存条件下弹药贮存失效数据进行预测,其次根据最小二乘拟合法,实现弹药贮存寿命评估模型的构建,再通过寿命评估模型,计算出不同方法下对应的贮存寿命。结果 通过不同模型的构建,4种预测方法与无优化条件下均能实现弹药贮存失效数据的预测,并且在规定可靠度,GA-BP和PSO-BP预测精度比另外2种方法更低。结论 GA-SVM与PSO-SVM更适合弹药贮存失效数据的预测,且效果更好。     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

Use of Multicriteria Decision Analysis to Address Conservation Conflicts

Conservation conflicts are increasing on a global scale and instruments for reconciling competing interests are urgently needed. Multicriteria decision analysis (MCDA) is a structured, decision‐support process that can facilitate dialogue between groups with differing interests and incorporate human and environmental dimensions of conflict. MCDA is a structured and transparent method of breaking down complex problems and incorporating multiple objectives. The value of this process for addressing major challenges in conservation conflict management is that MCDA helps in setting realistic goals; entails a transparent decision‐making process; and addresses mistrust, differing world views, cross‐scale issues, patchy or contested information, and inflexible legislative tools. Overall we believe MCDA provides a valuable decision‐support tool, particularly for increasing awareness of the effects of particular values and choices for working toward negotiated compromise, although an awareness of the effect of methodological choices and the limitations of the method is vital before applying it in conflict situations. Uso de Análisis de Decisiones Multicriterio para Abordar Conflictos de Conservación     

Influence of laneway support spacing on methane/air explosion shock wave

A laneway support system provides an available way to solve problems related to ground movements in underground coal mines, but also poses another potential hazard. Once a methane/air explosion occurs in a laneway, inappropriate design parameters of the support system, especially the support spacing, likely have a negative influence on explosion disaster effects. The commercial software package AutoReaGas, a computational fluid dynamics code suitable for gas explosions, was used to carry out the numerical investigation for the methane/air explosion and blast process in a straight laneway with different support spacing. The validity of the numerical method was verified by the methane/air explosion experiment in a steel tube. Laneway supports can promote the development of turbulence and explosion, and also inhibit the propagation of flame and shock wave. For the design parameters in actual laneway projects, the fluid dynamic drag due to the laneway support plays a predominant role in a methane/air explosion. There is an uneven distribution of the peak overpressure on the same cross section in the laneway, and the largest overpressure is near the laneway walls. Different support spacing can cause obvious differences for the distributions of the shock wave overpressure and impulse. Under comparable conditions, the greater destructive effects of explosion shock wave are seen for the laneway support system with larger spacing. The results presented in this work provide a theoretical basis for the optimized design of the support system in coal laneways and the related safety assessments.     

矿山企业安全管理决策支持系统的智能化

矿山安全工程技术人员需要开发使数学模型和安全信息便于操作的软件环境，并与人工智能技术集成一体。本文所介绍的矿山企业安全管理智能化决策支持系统ＳＭＤＳＳ，是专家系统与决策支持系统两者的集成。ＳＭＤＳＳ可用于企业安全评价、事故统计分析与查询、事故动态预测与预报、ＦＴＡ的计算、安全措施计划的编制、安全投资的最优分配。