Optimal locations of monitoring stations in water distribution systems under multiple demand patterns: a flaw of demand coverage method and modification

A flaw of demand coverage method in solving optimal monitoring stations problem under multiple demand patterns was identified in this paper. In the demand coverage method, the demand coverage of each set of monitoring stations is calculated by accumulating their demand coverage under each demand pattern, and the impact of temporal distribution between different time periods or demand patterns is ignored. This could lead to miscalculation of the optimal locations of the monitoring stations. To overcome this flaw, this paper presents a Demand Coverage Index (DCI) based method. The optimization considers extended period unsteady hydraulics due to the change of nodal demands with time. The method is cast in a genetic algorithm framework for integration with Environmental Protection Agency Net (EPANET) and is demonstrated through example applications. Results show that the set of optimal locations of monitoring stations obtained using the DCI method can represent the water quality of water distribution systems under multiple demand patterns better than the one obtained using previous methods.     

基于中国风能资源专业观测网的近地层风切变日变化特征

以中国风能资源专业观测网2009年5至10月10~120 m的梯度风观测数据分析了全国近地层风切变特征,结果表明:①风切变总体呈现规律性变化趋势,即:日出前和日落后切变指数较大,随着近地层温度升高,切变指数逐渐下降,近地层温度达到最高时,切变指数达到最低值,后随着日落、地面温度下降,切变指数逐渐上升,直到次日日出、日落周期;②因局地海(湖)陆分布的差异以及下垫面粗糙程度的不同,切变指数在各地的变化可归纳为如下几种类型:典型陆地型、海陆效应差异型、湖(河)陆效应差异型、特殊地形(峡谷)型、切变指数偏大型和特殊型;③从不同梯度间的风切变特征来看,低层(30 m附近)较为明显,而中高层(50 m和70 m)较小,说明30 m高度为我国近地层风速变化较为明显的层次。该研究资料序列短,可能在反映全国近地层风切变特征的普适性方面还存在一定的不足,但仍可作为我国风能资源丰富区近地层不同梯度间风切变分布和变化特征的重要参考,期望通过该研究的开展为风电场的布设及近地面层风能资源的利用提供技术依据。     

公路路网建设项目声环境影响评价研究

以甘肃省某市新区基础设施建设中的路网建设为例,根据公路路网的交通参数、道路地形地貌条件、路面设施等,利用导则推荐模式预测路网噪声贡献值。该新区路网中道路距离多在500m以上,不论道路平行还是相交,噪声影响以主干道为主,以次干道为辅,相互影响;根据路网噪声环境影响的特点,提出切实可行的声环境影响减缓措施。     

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。     

基于GA-ELM浆体管道输送临界流速预测模型研究

针对浆体管道输送临界流速预测难度大、精确度低等技术难题,提出了基于极限学习机(ELM)的临界流速预测模型,用训练集对模型进行训练,以验证集预测值的均方误差作为适应度函数,利用遗传算法(GA)对ELM模型参数进行优化,应用优化得到的ELM模型对预测集进行预测。以某矿山为例,模型参数优化结果如下:隐含层节点数L为400,输入权值ai、偏置向量bi最优组合下预测结果适应度为0.0201。采用优化的ELM模型对预测集进行预测,预测结果的最大相对误差x=3.96%,平均相对误差y=1.58%,对比BP神经网络(x=12.95%)和SVM模型(x=3.19%),表明ELM模型更加精确、高效。     

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.     

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.