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1.
Jacob A. Zwart Samantha K. Oliver William David Watkins Jeffrey M. Sadler Alison P. Appling Hayley R. Corson-Dosch Xiaowei Jia Vipin Kumar Jordan S. Read 《Journal of the American Water Resources Association》2023,59(2):317-337
Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making. 相似文献
2.
Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection. 相似文献
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本文针对政府环境治理面临政府失灵、市场失灵和社会低效三个困境,提出运用大数据方法创新治理路径。政府应用大数据提高环境治理过程中的事前、事中和事后监管效能,创新监管模式提升环境治理能力,改善政府失灵困境;依托大数据完善市场化调节机制,激励企业环境行为市场化,构建企业环境信用评价体系,改变企业环境信息披露模式,弥补市场失灵;利用大数据增进公众环境意识,建设公开、透明、平权化的环境信息交互体系,重视环境信息的舆情引导,促进环保价值理念传播,改变公众参与低效现状。进而提出完善大数据治理平台、技术和体系,构建政府、企业、公众共同参与的立体化环境治理模式,提高环境治理的精准化、精细化和智能化水平。 相似文献
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Despite sustained scholarly interest in post‐conflict states, there has not been a thorough review and analysis of associated methodology and the challenges of conducting research in these contexts. Addressing this gap, this paper directs attention to the particular effects of these settings on access and data quality and their ramifications for the resulting scholarship. It assesses the intrinsic challenges of performing fieldwork in these environments, drawing on both relevant social science literature and the authors’ experiences of carrying out research in Afghanistan and Timor‐Leste. The study demonstrates that the post‐conflict environment moulds research design and, consequently, influences how questions are answered as well as the questions asked. Moreover, it highlights ways to mitigate these issues. This work is of relevance to scholars planning to engage in field research and to researchers reflecting upon their work, as well as to policymakers who are considering undertaking programmes or commissioning research in post‐conflict areas. 相似文献
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为探析城市火灾风险的空间格局特征,提出科学的火灾防控体系,以合肥市为研究对象,基于火灾风险兴趣点(POI)数据、NPP/VIIRS夜间灯光影像数据、消防站与道路信息等多源数据,运用SAVEE模型和迭代方程评估城市火灾风险,并引入区位-配置(L-A)模型,从不同目标情境优化消防站点布局。研究表明:火灾高风险区主要集中于合肥市二环以内的建成区,尤其是老城区、合肥西站片区、政务区、科学城和东部新中心等区域;现有48座消防站对研究区内的POI覆盖效果较好,覆盖率为86.76%,而对NPP/VIIRS覆盖效果一般,覆盖率仅为55.94%,覆盖率在合肥市东北部、西北部和南部部分地区仍有不足;基于最大化覆盖模型,规划新增44座消防站,明显提升5 min响应时间的覆盖率,可进一步夯实城市消防安全。 相似文献
7.
了解O3污染的垂直分布对于充分理解O3在大气中的扩散和输送具有重要意义.本研究利用最优插值法实现了高塔与激光雷达O3观测数据的融合,并基于垂直观测融合数据对2021年10月广州市一次O3污染过程进行分析,结果表明:(1)不同时刻的O3浓度均大致呈现出随高度上升先升后降的变化趋势,平均相对高值主要分布在300~500 m,最高值出现在400 m附近.(2)结合边界层高度分析可知,白天的O3生成和扩散基本均在边界层以内进行,夜间普遍存在O3残留问题,而在污染日尤其显著,表明白天光化学反应生成的高浓度O3是夜间残留层中O3的来源.(3)污染期间,不同大气污染物形成了不同的垂直分层,具体表现为较高浓度的PM2.5和NO2在中、低层积累,而高层(约200~600 m)则维持高浓度O3的污染垂直分布结构.推测原因在于南北气流对峙及夜间稳定... 相似文献
8.
Ian Davis 《Disasters》2019,43(Z1):S61-S82
This reflection, based on a keynote address to celebrate the fortieth anniversary of Disasters on 14 September 2017, begins by considering some of the prevailing assumptions that existed at the time of the journal's creation. Next is a summary of significant milestones during the past 40 years, covering major global trends, changing disaster impacts, and key developments in disaster risk management. Contrasting approaches in the first and fourth decade of the journal's history are then followed by examples of changes in terminology in the disaster field as an indication of shifting values. The paper goes on to explore the context of 2017, with observations of problem areas such as the loss of knowledge, failures to tackle the root causes of disasters, shortfalls in international assistance, and some negative attitudes. It closes by evaluating some current positive occurrences, including a reduction in casualties, sharing of experience, technological advances, and dramatic improvements in disaster warning. 相似文献
9.
Air pollution and other environmental hazards are often imperceptible and need to be made publicly visible. The paper argues for the importance of visualizations in drawing public attention to imperceptible hazards and in providing the public with access to empirical data describing the risks. It also argues for critical inquiry into hazards’ selective visibility as it is produced by visualizations. The impact of visualizations and their selective visibility are considered through the example of a public art project called Particle Falls installed in 2014 in Pittsburgh, a city with a long history of both ignoring air pollution and working to ameliorate this problem. I examine the impact and selective visibility of Particle Falls by considering the underlying production of data, as well as context and support systems for this visualization, and by comparing it with other visualizations of local air quality. 相似文献
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为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性,基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线(ROC曲线)对比分析随机森林(RF)、支持向量机(SVM)、决策树(DT)与神经网络(NN)4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制(Y1)、职业危害预防(Y2)、财产损失3类预测目标(Y3),RF方法均能达到较高的准确率及稳定性,具有较优的预测性能。根据特征重要度(FI)排序,明确对组织事故水平影响最显著的因素为安全实践活动认知(SC5)及安全管理程序文件(SMS3),FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据,同时为企业安全工作的规划设计提供思路。 相似文献