Bibliometric analysis of ecosystem monitoring-related research in Africa: implications for ecological stewardship and scientific collaboration

Regional ecosystem monitoring is a central form of knowledge sharing and collaboration amongst scientists and decision makers on environmental health, land use change, and science-policy development. Despite the proliferation of such research networks on long-term monitoring on many continents, little has been achieved in Africa. This study aims to assess and examine the spatiotemporal trend and categorical patterns in ecosystem monitoring-related research in Africa for the benefits of conserving biodiversity and sustaining natural resource sectors for well-being and livelihood security, environmental planning, and ecological stewardship. A systematic review was conducted using bibliometric tools. Based on a set of search terms and peer-reviewed publications retrieved from various ecosystem monitoring networks and journal databases, further analysis was conducted using social network approaches, mapping tools, and content analysis. About 1442 scientific publications on ecosystem monitoring and related research were documented from 1987 to 2014 mostly published in English. The number of publication increased progressively since 1992 after the Convention on Biodiversity was signed and this trend peaked till 2008. South African Journal of Science was the most leading journal and Nature the most cited. Internationally coauthored and collaborative articles represented majority of the findings with the United Kingdom at the central position in the research network due to colonial relationships. Regional collaboration amongst countries is limited owing to language barriers and other institutional constraints such as funding and short-term projects. These findings have implication for prioritizing national and regional policies toward biodiversity science and its contribution to human well-being, food security, and global change responses.     

Exploration of spatial morphometry and socioeconomic variables in modelling urban land use change

The incorporation of land use (LU) data with socioeconomic data is a main issue in modelling. This is as a result of difference in data model and scale. This study proposed and tested the change–pattern approach, which allows the incorporation of these data sets in modelling LU change. Focusing on LU dynamics for a selected part of the Thames Gateway within the City of London, the approach tested two different methods of input selection for the modelling operations. Variables selected from these two methods serve as inputs into several neural networks tested in order to identify the direction of change for each of the LU types within the study area. The result shows that direction of LU change across the study area could be identified when spatial morphology of the area and socioeconomic variables are considered. Some classes of change could be identified fairly accurately using landscape metrics indicating level of fragmentation, extent of LU patches, shape complexity of LU patches in combination with some socioeconomic variables.     

基于自适应遗传小波神经网络的水质评价建模

水环境污染过程的非确定性和非线性,使得传统的水质评价方法存在局限性。为了提高水质评价的准确性,提出了一种基于改进小波神经网络(wavelet neural network,WNN)的水质评价模型。采用自适应遗传算法(adaptive genetic algorithm,AGA)对小波神经网络的初始权值进行优化,再通过小波神经网络算法对网络进行训练,最后对训练好的网络展开测试。仿真结果表明,自适应遗传算法和小波神经网络的结合提高了网络的训练效率,该方法可以用于水质评价建模,并且评价结果具有较高的精度和准确性。     

基于小波神经网络的地下水流数值模拟模型的替代模型研究

以吉林西部为研究区,建立地下水流数值模拟模型,分别应用蒙特卡罗方法和拉丁超立方方法在研究区10个县(市)开采量的可行范围内进行采样,经对比选择拉丁超立方抽样结果得到输入(开采量)—输出(水位降深)数据集,建立小波神经网络模型作为地下水流数值模拟模型的替代模型,而后对替代模型有效性作误差分析,并与多元非线性回归替代模型进行对比.结果显示,2种替代模型在功能上都能逼近地下水流数值模拟模型,但小波神经网络模型得到的水位降深均值和水位降深剩余标准差与模拟模型计算结果的相对误差分别低于多元非线性回归模型76%和45%,说明小波神经网络模型更适合作为地下水流数值模拟模型的替代模型,这为减少优化模型求解过程中直接调用模拟模型所造成的计算负荷提供了一种有效的替代方法.     

一种基于GIS的地下洞室群风网解算可视化研究

为了实现地下洞室群火灾网络模拟的可视化,利用地理信息系统及C#语言研究开发了一种风网解算系统。该系统能实现通风网络图的绘制及自动编号、解算及显示火灾烟气可能蔓延的区域,并可为相关部门制定火灾应急救援预案时提供支持     

基于多源数据融合的石油罐区安全监控模型

由于单一传感器在石油罐区安全监控中容易受到外界因素影响从而产生误差,为提高传感器检测结果的可靠性和罐区安全监控预警的准确性,基于多源数据融合技术,建立罐区安全状态预警模型。首先,介绍了多源数据融合技术的3个层级:数据级融合,特征级融合和决策级融合,以及目前各领域常见的数据融合方法;其次,建立了基于最优加权融合算法的一级融合模型和基于BP神经网络算法的二级融合模型;最后,得到石油罐区安全监控数据融合模型,并为进一步的实践应用打下了理论基础。     

基于ANP-DEA的城轨网络应急演练组合评价模型

应急演练综合评价是衡量应急演练效果好坏的关键。以城轨网络应急演练综合评价为研究对象,将城轨网络应急演练划分为演练准备、演练实施、演练总结三个阶段。针对评价指标间存在的关联关系,采用网络层次分析法(ANP)进行定量化处理,计算得到各评价指标综合权重,进而得到三个演练阶段的重要度。利用数据包络分析方法(DEA),分别计算三个阶段不同城轨线路应急演练的排序值,结合不同阶段的重要度,构建得到城轨网络应急演练的组合评价模型,实现对城轨网络应急演练效果的综合评价。结合算例,应用ANP-DEA组合模型,对某城轨网络的应急演练效果进行了综合评价。     

基于灰色神经网络优化组合模型的火灾预测研究

为了提高火灾事故预测的精度,根据我国火灾事故数据样本较小,波动性较大的特点,将遗传算法优化的灰色无偏预测模型与遗传算法优化的BP神经网络模型结合起来,建立灰色神经网络优化组合模型,充分发挥无偏灰色预测模型适用于小样本的数据预测的优势与BP神经网络处理非线性问题的优点。分别采用遗传算法优化后的无偏灰色GM（1,1）模型、遗传算法优化的BP神经网络预测模型与灰色神经网络优化组合模型对我国1998-2008年的火灾事故进行拟合,并对2009-2011年的火灾事故发生数进行预测。结果表明:灰色神经网络优化组合模型的预测误差最小,精度最高,适用于火灾事故的预测。     

矿井通风网络复杂度与风机控制力量化计算

为了探究矿井通风网络复杂度与风机控制力的关系,提出分支关联度、最小距离、通风网络复杂度和风机控制力的概念。利用vCad2.0进行数字实验,对网络复杂度与风机控制力的关系进行定量研究。结果表明:随着网络复杂度的增加,矿井通风系统的有效风量率减小,且认为该值减小到60%时,视为风机控制力的极限,此时网络拓扑结构为风机所能控制的最大网络复杂度。地面集中供风时,同时工作的中段数量存在上限,最大值为7。将结论用于某有色金属矿进行实证研究,找出该矿深部中段通风困难的原因,为矿井进一步整改提供理论依据。     

基于改进贝叶斯神经网络的航空公司安全敏感性分析

针对当下航空公司安全质量管理体系(Quality Management System,SQMS)中风险识别与可靠性改进的问题,提出了基于区间数学改进的贝叶斯神经网络的灵敏度分析方法。利用区间数学理论分析贝叶斯神经网络中各指标与整体安全质量状况的扰动关系,实现指标灵敏度分析。通过东方航空公司的实例分析,发现在对指标进行人工干预时组合指标干预效果较好,且安全管理体系实施后指标的灵敏度有明显向好的方向变化的趋势。