基于无线传感器网络的市政下水道可燃气体监测系统设计及应用

为防止下水道可燃气体积聚引发爆炸事故,利用无线传感器网络技术设计了市政下水道可燃气体监测系统.实测结果表明:气体传感器监测数据相对误差在2.0%以内,无线数据收发设备的年发送成功率达到98.0%,满足工程运用需求,系统具有较高的可靠性和稳定性.结合无线网络覆盖范围广、实时在线的特点,该系统实现了对不同区域下水道内可燃气体浓度的远程监测,为下水道的安全预警提供了有效依据.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

天津市典型河网区沉积物中重金属分布及生态风险评价

选择天津市典型河网区为研究对象,分析了沉积物中6种重金属(Cr、Cu、Mn、Ni、Pb和Zn)的分布特征,并采用富集系数法和潜在生态危害指数法分析了重金属的来源,进而评价了其生态风险。结果表明,表层沉积物中Cr、Cu、Mn、Ni、Pb和Zn的平均含量分别是58.18、23.52、524.60、22.93、25.24和49.51 mg/kg,其中Cr、Mn和Ni含量均低于天津市土壤背景值,而Cu、Pb和Zn含量在部分样点高于背景值;在垂直分布上,沉积物中Cr和Ni的含量相对稳定,而Cu、Mn、Pb和Zn的含量从底层到表层均先增加后降低。重金属富集系数(EF)分析显示,河网区表层沉积物中Cu和Zn在个别样点以及Pb在近一半样点存在人为输入过程(EF1.5),与接纳上游及区域的生活污水、农田退水有关,而Cr、Mn和Ni均来自自然源。重金属综合潜在生态危害指数(RI)评价表明,整个河网区表层沉积物为轻微生态危害水平,对区域的水环境质量不构成威胁。     

城市绿地系统建设的生态对策——以长春市为例

绿色空间是所有城市实现可持续发展的物质载体和市民接触自然的主要场所。几乎所有城市不同程度地存在着绿地被挤占、破坏、污染严重等各种问题。以长春市为例,提出了大城市进行绿地系统建设的总量控制、构建经色空间网络体系等生态对策,并针对大城市内部用地紧张的矛盾,探讨了市区内部的绿化用地潜力。     

校园网建设的前期规划研究

本文对建设校园网的前期准备工作作了详细的阐述,对各阶段的工作详细作了规划,对于准备建设校园网的学校具有指导意义.     

A new intelligent electronic nose system for measuring and analysing livestock and poultry farm odours

This paper introduces a new portable intelligent electronic nose system developed especially for measuring and analysing livestock and poultry farm odours. It can be used in both laboratory and field. The sensor array of the proposed electronic nose consists of 14 gas sensors, a humidity sensor, and a temperature sensor. The gas sensors were especially selected for the main compounds from the livestock farm odours. An expert system called “Odour Expert” was developed to support researchers’ and farmers’ decision making on odour control strategies for livestock and poultry operations. “Odour Expert” utilises several advanced artificial intelligence technologies tailored to livestock and poultry farm odours. It can provide more advanced odour analysis than existing commercially available products. In addition, a rank of odour generation factors is provided, which refines the focus of odour control research. Field experiments were conducted downwind from the barns on 14 livestock and poultry farms. Experimental results show that the predicted odour strengths by the electronic nose yield higher consistency in comparison to the perceived odour intensity by human panel. The “Odour Expert” is a useful tool for assisting farmers’ odour management practises. Supported by Ontario Pork, Natural Sciences and Engineering Research Council (NSERC), and Ontario Ministry of Agriculture and Food (OMAF) of Canada.     

基于深度学习和物联网技术的智慧污水管控系统

在智慧城市建设中,水资源的智慧化管理是其不可或缺的一环,智慧水务对促进城市的长期健康发展意义重大.以青岛市某精细化工厂为例,将人工智能技术与物联网技术相结合并运用于该化工厂智慧污水管控系统中,通过一系列传感器采集动态数据,实现对水质质量的实时监测.该智慧污水管控系统应用深度学习中的长短期记忆(Long Short-Te...     

基于BP神经网络的藻类水华预测模型研究

以宁波大学校内池塘2009年3—10月间30周的监测数据为基础,运用BP人工神经网络方法构建预测模型,探求颤藻生物量与总氮、总磷、透明度等6项环境因子之间的关系,选出最佳预测模型,并对模型进行敏感度分析。结果显示:①BP神经网络模型对颤藻生物量预测值与实测值之间拟合程度良好,相关系数达到了0.984,说明BP神经网络模型可以用于水体中藻类水华的短期预测。②通过对构建的BP神经网络模型进行敏感度分析,阐明了宁波大学校内池塘藻类水华的主要驱动因素,并指出控制水体的pH是宁波大学校内池塘藻类水华防治工作的重点。     

新疆城市环境空气PM_(10)自动监测仪器质控巡检工作探析

随着城市环境空气质量自动监测业务现代化的飞速发展,新疆城市共安装38套PM10环境空气自动监测系统。本文通过对新疆19个城市空气自动监测系统PM10的巡检和质控考核,发现近几年来该系统运行维护中的诸多问题,从而提出仪器维护注意事项,以期提高新疆城市PM10监测数据质量,准确反映环境空气质量状况,为环境管理和政府决策提供更加科学、准确、可靠的基础监测数据。     

新标准下环境空气质量监控网络完善实践 ——以珠三角地区佛山市为例

介绍珠三角及佛山市新标准监测网络结构,从拓宽网络有效覆盖范围、提升网络对污染研究的支撑能力、以及巩固数据实时发布工作基础等多角度分析了网络完善需求,提出了下一阶段网络建设的重点措施建议,并介绍了佛山市目前计划进行的网络完善工作。