Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models

Aquatic biogeochemical models are widely used as tools for understanding aquatic ecosystems and predicting their response to various stimuli (e.g., nutrient loading, toxic substances, climate change). Due to the complexity of these systems, such models are often elaborate and include a large number of estimated parameters. However, correspondingly large data sets are rarely available for calibration purposes, leading to models that may be overfit and possess reduced predictive capabilities. We apply, for the first time, information-theoretic model-selection techniques to a set of spatially explicit (1D) algal dynamics models of varying parameter dimension. We demonstrate that increases in complexity tend to produce a better model fit to calibration data, but beyond a certain degree of complexity the benefits of adding parameters are diminished (the risk of overfitting becomes greater). The particular approach taken here is computationally expensive, but several suggestions are made as to how multimodel methods may practically be extended to more sophisticated models.     

城市轨道交通行车安全保障信息系统的研究

在分析城市轨道交通安全系统现状的基础上 ,笔者阐述了建立行车安全保障信息系统的必要性 ;提出需先期建立综合调度指挥中心 ;而后 ,重点介绍了作为该中心基本组成部分的行车安全保障信息系统及其各个子系统的构成情况 ;进而对监测子系统的设计进行初步研究。城市轨道交通行车安全保障信息系统可以增强事故处理实时性 ,也可在一定程度上减轻事故损失 ,从而指导安全管理工作 ,同时也是提高城市轨道交通自动化水平的一个创新性探索。     

基于GIS的东莞市河流有机污染特征分析

基于水样采集分析和GIS数据处理技术,探讨了东莞市河流有机污染状况及空间特征。结果表明,东莞市各个不同级别的河流NH4+-N和TP污染都很严重,主要原因是经济发展迅速、废水排放量大、污水管网建设滞后等。运河干流下游段和低级别支流水质污染尤为严重,前者主要受到上游废水排放以及河道淤积的影响较大,而后者则与直接受纳乡镇工农业和生活污水有关,也与水处理设施落后有较大关系。     

Strategic environmental disclosure: Evidence from the DOE's voluntary greenhouse gas registry

Although mandatory disclosure programs have been studied extensively, strategic voluntary environmental disclosures by firms are not well understood. We study the motivations for and impacts of firms' strategic disclosure of greenhouse gas reductions to the US government. We first model firms' joint abatement and disclosure decisions, incorporating both economic and political incentives. We then use data from the Department of Energy's Voluntary Greenhouse Gas Registry to compare reported reductions to actual emissions. We find that participants in the program engage in highly selective reporting: in the aggregate, they increase emissions over time but report reductions. In contrast, non-participants decrease emissions over time. Participants tend to be large firms facing strong regulatory pressure; pressure from environmental groups reduces the likelihood of participation, suggesting such groups viewed the program as a form of greenwash. Participating in the 1605(b) program had no significant effect on a firm's changes in carbon intensity over time.     

Realizing an effectiveness revolution in environmental management

The environmental movement of the 20(th) century has evolved into a large, diverse and well-financed global community that is increasingly required to prove its worth. Though the environmental sector collects and uses data to determine the status of ecological and social systems, the effectiveness of the programs and policies it uses to affect this status remains largely untested. As governments and donor institutions insist on greater transparency, accountability and evidence of what works and what does not, much is being learned from other fields (e.g. health services, education, international development) and increasingly sophisticated approaches are emerging to manage effectiveness. For example, program evaluation, adaptive management, and systematic review provide frameworks and methods to collect and use information to measure and improve performance. However, the critical data and collaborations necessary for an effectiveness revolution are marginalized by technical, cultural and political obstacles. Learning from other fields, the environmental sector must exploit key leverage points, such as flows of information and self-organization, to overcome impediments and create incentives to initiate and realize an era of effectiveness in environmental management.     

基于气象预测数据的中国洪涝灾害危险性评估与预警研究

基于自然灾害风险理论,借助GIS强大的空间分析功能,采用归一化和层次分析法,对中国全国范围尺度进行了洪涝灾害危险性评估。通过对洪涝灾害危险性因子分析,分别提取当天降雨量、前3 d降雨量、地形高程、地形标准差、河湖网络等因素作为评估因子,提出了各因子危险性指数计算方法,以及全国洪涝灾害危险性指数计算模型公式。根据统计分析危险指数的最小值、最大值,结合历史灾情,利用阈值分割法确定了风险等级分割值分别为0.3、0.45及0.6,将洪涝灾害等级划分为高风险、中风险、低风险与无风险四个等级,从而建立了类似于天气预报模式的全国洪涝灾害危险性评估模型,并以2010年8月22日为例进行了洪涝灾害危险等级评估的实际应用。最后,基于洪涝灾害的危险性评估的结果,结合危险区内人口分布、交通设施等基础数据,生成相应的预警产品。对可能发生的洪涝灾害发出预警与预测,为危险区人们乃至相关政府管理部门采取有效的防灾、减灾措施提供参考依据。     

Global information sampling in the honey bee

Central to the question of task allocation in social insects is how workers acquire information. Patrolling is a curious behavior in which bees meander over the face of the comb inspecting cells. Several authors have suggested it allows bees to collect global information, but this has never been formally evaluated. This study explores this hypothesis by answering three questions. First, do bees gather information in a consistent manner as they patrol? Second, do they move far enough to get a sense of task demand in distant areas of the nest? And third, is patrolling a commonly performed task? Focal animal observations were used to address the first two predictions, while a scan sampling study was used to address the third. The results were affirmative for each question. While patrolling, workers collected information by performing periodic clusters of cell inspections. Patrolling bees not only traveled far enough to frequently change work zone; they often visited every part of the nest. Finally, the majority of the bees in the middle-age caste were shown to move throughout the nest over the course of a few hours in a manner suggestive of patrolling. Global information collection is contrary to much current theory, which assumes that workers respond to local information only. This study thus highlights the nonmutually exclusive nature of various information collection regimes in social insects.     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

Using Risk-Based Analysis and Geographic Information Systems to Assess Flooding Problems in an Urban Watershed in Rhode Island

This article provides an overview of the use of risk-based analysis (RBA) in flood damage assessment, and it illustrates the use of Geographic Information Systems (GIS) in identifying flood-prone areas, which can aid in flood-mitigation planning assistance. We use RBA to calculate expected annual flood damages in an urban watershed in the state of Rhode Island, USA. The method accounts for the uncertainty in the three primary relationships used in computing flood damage: (1) the probability that a given flood will produce a given amount of floodwater, (2) the probability that a given amount of floodwater will reach a certain stage or height, and (3) the probability that a certain stage of floodwater will produce a given amount of damage. A greater than 50% increase in expected annual flood damage is estimated for the future if previous development patterns continue and flood-mitigation measures are not taken. GIS is then used to create a map that shows where and how often floods might occur in the future, which can help (1) identify priority areas for flood-mitigation planning assistance and (2) disseminate information to public officials and other decision-makers.     

基于加权信息量和迭代自组织聚类的地质灾害易发性评价

以三峡库区忠县-方斗山地区为研究对象,首先选取坡度、坡向、坡高、岩土体类型、坡体结构、构造、水的作用、人类工程活动等8个评价因子建立地质灾害易发性评价指标体系,并对因子状态进行分级;然后,采用层次分析法计算各指标因子的权重,应用加权信息量模型进行地质灾害易发性评价;再者,利用迭代自组织聚类模型对地质灾害易发性评价结果进...