基于AGA-BP神经网络的采空区危险性评价

针对采空区危险性评价的影响因素众多且关系复杂的特点,提出了基于AGA-BP神经网络算法评价采空区危险性。将岩体结构、地质构造、岩石抗压强度等13个影响因子作为神经网络输入,采空区危险性等级作为输出,建立一个采空区危险性评价的BP神经网络模型;采用自适应遗传算法(AGA)对BP网络的初始权值和阈值进行全局寻优,将寻优结果回代入网络中进行训练并预测得出采空区危险性等级;利用其它智能算法与该预测结果做出比较,以验证AGA-BP算法的有效性及优越性。结果表明:该算法的优化效果明显,同时在训练时间与预测精度上较其它智能算法有突出的优势,是一种在采空区危险性评价方面值得推广的新方法。     

外部安防系统在海洋石油固定平台中的应用

为了弥补传统的海洋石油固定平台内部安防系统的不足,达到海洋石油固定平台全天候自动监测、自动报警、无人值守、主动防御、预防为主的目的,以便提早发现灾害或事故的苗头,提供及时报警,并采取适当的预防措施。根据主动防御、准确测报、防范未然和规避事故的原则方针,按区域警戒与要地防范相结合的方法,介绍了外部安防系统在海洋石油固定平台中的应用,包括六个子系统、工作流程、各个子系统在海洋石油固定平台中的应用以及特点和价值,从监控、应急、监管等多角度出发,实现了一体化的安全监控。     

基于小波分解及遗传BPNN耦合模型的地表水As浓度预测研究

随着工业的快速发展,水体中污染物超标事件时有发生,造成了较严重的水环境污染问题.水环境监测与预报是环境科学研究的重要内容.为了实现地表水砷（As）污染的准确预报,本研究提出小波分解、遗传算法与BP人工神经网络的耦合建模方法,并结合某河流监测站1998—2016年共19年的地表水质监测数据,通过皮尔逊相关系数和信息指标评价法对模型输入变量进行筛选,最后对比分析了在不同水质参数输入情况下BP人工神经网络（BPNN）、遗传算法改进的BPNN（GABP）、小波-遗传BPNN耦合模型（W-GABP）对后6年（2011—2016年） As浓度预测结果的均方根误差（RMSE）、决定系数（R²）、平均绝对百分比误差（MAPE）,以确立最优模型.结果表明：①多水质参数BPNN、GABP与W-GABP耦合模型预测结果的MAPE分别为17.51%、15.98%、14.46%,单水质参数BPNN、GABP与W-GABP耦合模型预测结果的MAPE分别为18.78%、16.74%、7.83%;②小波分解数据前处理及遗传算法均能较大程度地提高预测模型的精度;③对于地表水水质预报,需对比不同模型在不同输入变量下的预测结果,以获得最佳的预测精度.单水质参数输入的W-GABP耦合模型能较准确地预报地表水As浓度的变化情况,对数据缺乏地区水质监控和地表水As污染防治具有重要意义.     

乡村路网对三峡库区社区水平森林结构的影响

道路作为人为扰动介质被认为是短时空尺度森林景观演化或用途转换的主要驱动力之一,体现为明显的“通道-阻隔”效应。论文以重庆市石柱县西沱镇为例,以3期遥感影像和调研数据为基础,利用ArcGIS的缓冲叠加功能,基于路网动态变化角度,从类型、林龄、起源等主要森林结构方面分析路网变化对森林结构变化的影响。结果表明：1）研究区道路数量先增加后减少,道路质量以等外公路和村级公路的改善为主。2）道路对森林景观干扰性明显。森林从类型、起源、林龄三方面在道路缓冲区内的破碎度均高于非缓冲区内的破碎度。1992—2002年及2002—2014年两个时期相比,前一时期其他灌木林、幼龄林以及萌生林大幅减少,基本表现为道路缓冲区内减少量大于非缓冲区内减少量。后一时期乔木林、中龄林以及萌生林有所增加,其他灌木林和成熟林轻微减少,其中,乔木林在道路缓冲区内增加量大于非缓冲区内增加量,中龄林及萌生林则表现为道路缓冲区内增加量弱于非缓冲区内增加量。3）道路影响域范围内,在无政策性干预条件下,道路的增加和质量的提高很大程度上导致森林景观的退化,低级路段的消失则有助于森林景观的恢复。     

输水情景下白洋淀好氧反硝化菌群落对溶解性有机物的响应

溶解性有机物（DOM）是影响微生物群落演变的重要因素,而生态输水是白洋淀的一个重要特征,为了探究输水情境下DOM对好氧反硝化菌的影响,本文结合水体DOM的组分解析和好氧反硝化高通量测序技术,进行了水体好氧反硝化菌对DOM的响应研究.结果显示,白洋淀水体DOM的相对浓度存在显著差异,河口区要低于内部区;DOM呈现出较强自生源特征,河口区具有更高的分子量和更强的腐殖化;平行因子法解析出3种类蛋白组分和1种类腐殖质组分,类蛋白组分占比达到35.64%~96.38%,与荧光区域积分得到类蛋白占主体的结果相一致.与此同时,该时期水体好氧反硝化菌主要属于变形菌门（Protebacterice）,主要包括Cupriavidus、Aeromonas、Thauera、Shewanella和Pseudomonas,与随机森林筛选出的指示物种相一致;网络分析得到35个网络关键节点,主要隶属于Thauera、Cupriavidus以及Unclassified_bacteria;冗余分析（RDA）显示类腐殖质物质是影响水体整体好氧反硝化菌群落组成的因子,类蛋白物质是影响指示物种群落和关键节点群落结构分布的重要因素.综上可知,水体溶解性有机物中类蛋白组分可以作为筛选适于生态输水期水体特征的耐低温高效好氧反硝化菌的碳源选择.     

能源安全综合评价方法探讨

能源安全是指一个国家或地区可以获取稳定、足量、经济、清洁的能源供给,以满足需求,保障经济社会稳健运行和持续、协调发展的能力和状态。它可用加权综合模型计算得来的"能源安全度"指数进行描述,取值范围最高为1,最低为0,分值越高越安全。研究表明,现实情景下,我国能源安全度平均为0.712 5,处于"基本安全"状态。未来发展规划得当,措施得力,到2020年、2030年能源安全度可提高到0.841 1、0.847 7,进入"安全"区间。若遇针对我国的能源封锁,则能源安全度可能会降至0.552 0的"不安全"区间。解决我国能源安全问题的关键,主要是增加战略储备、实行供应多元化、加强地质勘查、发展替代能源、提高能源效益,而且能源外交也要摆在显著位置。     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Application of a temperature-dependent von Bertalanffy growth model to bullhead (Cottus gobio)

The most studied and commonly applied model of fish growth is the von Bertalanffy model. However, this model does not take water temperature into account, which is one of the most important environmental factors affecting the life cycle of fish, as many physiological processes that determine growth, e.g. metabolic rate and oxygen supply, are directly influenced by temperature. In the present study we propose a version of the von Bertalanffy growth model that includes mean annual water temperatures by correlating the growth coefficient, k, explicitly and the asymptotic length, L_∞, implicitly to water temperature. All relationships include parameters with an obvious biological relevance that makes them easier to identify. The model is used to fit growth data of bullhead (Cottus gobio) at different locations in the Bez River network (Drme, France). We show that temperature explains much of the growth variability at the different sampling sites of the network.     

Multiple structural modifications to dendritic ecological networks produce simple responses

Structural modifications to landscapes affect the ability of organisms to access different habitat patches. There exist, however, very few general methods by which to relate modifications to expectations of effects, and even fewer that enable understanding of how multiple modifications may interact. In the absence of any guiding principles, ecologists have assumed that interactions will result in complex landscape-scale effects. One way of understanding such effects is through rendering a landscape as a graph or network, among the simplest of which are dendritic networks typified by stream systems. Yet even for stream networks, there are no known general principles concerning the nature of interactions between multiple modifications. We developed a model to describe the ability of fish to access and use different habitat patches within dendritic networks. We used mathematical and numerical analyses of the model to investigate how the habitat value of a network is affected by changes in connectivity and habitat quality, and then to examine interactions between multiple modifications. Rather than showing complex interactions, our analytic and simulation-based results show that the combined effect of multiple modifications approximately equals the sum of individually predicted effects. Dendritic networks thus appear to respond far more simply to multiple modifications than has previously been assumed. These results have implications for stream management planning, and offer a firm foundation from which to better understand population processes within dendritic networks.     

基于人工神经网络法的绿色建筑评价

以实际建筑物为例,介绍了用层次分析法建立绿色建筑评价模型的过程,并分别用层次分析法和人工神经网络法对实际建筑物进行了评价。评价结果显示,人工神经网络法与层次分析法相对误差不到0.5%,表明人工神经网络法作为一种客观科学的评价方法,应用于绿色建筑的评价,能有效降低主观因素带来的影响,会使结果更具有客观性。