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1.
Peiman Parisouj Hadi Mohammadzadeh Khani Md Feroz Islam Changhyun Jun Sayed M. Bateni Dongkyun Kim 《Journal of the American Water Resources Association》2023,59(2):299-316
Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing. 相似文献
2.
Abhiram S. P. Pamula Hamed Gholizadeh Mark J. Krzmarzick William E. Mausbach David J. Lampert 《Journal of the American Water Resources Association》2023,59(5):929-949
Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality. 相似文献
3.
Tienan Ju Mei Lei Guanghui Guo Jinglun Xi Yang Zhang Yuan Xu Qijia Lou 《Frontiers of Environmental Science & Engineering》2023,17(1):8
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Introduction: In low-cycling countries, motor-vehicle traffic and driver behavior are well known barriers to the uptake of bicycles, particularly for utility cycling. Lack of separation between cyclists and faster-moving traffic is one key issue, while attitudes of drivers toward and/or harassment of cyclists is another. Cyclist-related driver education has been recommended as a means to improve driver-cyclist interactions. Methods: The driver licensing process provides an opportunity for such education. The Cycle Aware module was developed to test and enhance novice drivers’ knowledge of interacting safely with cyclists. It was piloted across three Australian jurisdictions targeting both novice and experienced drivers. Participants were asked to complete the Cycle Aware module and an accompanying survey. A total of 134 novice and 97 experienced drivers completed the survey with 42 novice and 50 experienced drivers going on to complete the module. Results: Both groups of drivers scored equally well in the module but the very youngest and very oldest participants were more likely to have some incorrect responses. We did not find any relationship between correct module scores and attitudes toward cyclists. Survey results showed both novice and experienced drivers had somewhat positive attitudes toward cyclists. The two cohorts differed on several attitude questions. Sixty percent (60%) of novices compared to 30% of experienced drivers reported feeling concerned when sharing the road with cyclists, and novices were less likely to agree that cyclists had a right to use the roads. Conclusions and practical applications: The analysis suggests novices need to be better equipped to share roads confidently with cyclists and to recognize cyclists as legitimate traffic participants. 相似文献
6.
为利用视频数据对空管员违规行为进行智能化分析,降低不安全事件发生率,提出2阶段的违规行为识别模型(AR-ResNeXt),基于实地调研构建空管员视频数据集,利用最小化动态多实例学习损失函数和中心损失函数,获得违规行为检测的判别特征表示,结合异常回归网络和ResNeXt网络,完成对空管员违规行为的时序区间检测与动作分类。研究结果表明:AR-ResNeXt模型在自制数据集中,其帧级AUC达到82.9%,分类准确率达到87.8%,可准确识别空管员发生违规行为的时序区间并进行分类,研究结果可为保障空中交通安全奠定基础。 相似文献
7.
Although researchers have highlighted the importance of diversity beliefs (i.e., team members' perceived value of diversity) for the elaboration of information in teams, little attention has been paid to whether and how diversity beliefs can be shaped. Drawing on theory and research on team diversity beliefs, we propose that diversity beliefs are more effectively influenced by interventions using a promotion (compared with a prevention) focus toward diversity and personal testimonial (compared with factual) knowledge. Results from an experiment conducted with 175 teams revealed that both a promotion focus and personal testimonial knowledge independently contributed to more positive diversity beliefs and consequently increased team elaboration of task-relevant information as well as integration of different perspectives. Our results reveal key factors that can influence diversity beliefs and underscore the pivotal role of diversity beliefs in improving the extent to which team members elaborate information and integrate diverse perspectives. 相似文献
8.
Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios. 相似文献
9.
我国工程机械排放控制起步较晚.为研究实际工况下工程机械的PM2.5排放特性及其碳质组分构成,采用便携式颗粒物稀释采样系统,对3台工程机械(2台挖掘机和1台装载机)在不同典型工况(行驶、作业和怠速)下的PM2.5及其碳质组分〔OC(有机碳)和EC(元素碳)〕的现场排放特征进行了测试.结果表明:沃尔沃挖掘机、山河智能挖掘机的PM2.5排放因子(基于燃油)分别为1.85~3.26和1.56~2.62 g/kg,厦工装载机的PM2.5排放因子为0.98~1.48 g/kg.不同工况对PM2.5排放因子影响较大,怠速工况下PM2.5排放因子是行驶工况下的1.49~1.76倍.工程机械排放的PM2.5中,碳质组分是最主要的成分,其质量分数高达71.0%~84.5%.其中,w(OC)为44.6%~72.0%,在怠速工况下最高;w(EC)则为8.6%~30.9%,在行驶工况下较高.测试工程机械的PM2.5排放水平较高,因此应尽快加强工程机械排放的污染防治. 相似文献
10.
(过冷)液体蒸气压(PL)是评价化学品在环境中分配、迁移和归趋行为的重要参数。PL具有较强的温度依附性。发展一种能够精确预测不同环境温度下化学品PL的方法,有助于填补化学品生态风险评估的大量数据缺失。本研究收集整理了661种有机化合物在不同温度下(200~830 K)共计10 478个log PL值。在此基础上,采用偏最小二乘(PLS)回归和支持向量机(SVM)方法,构建了PL的线性和非线性预测模型。结果表明:2种模型均具有良好的拟合度、稳健性及预测能力,SVM模型的预测性能略高于PLS模型(PLS:R2adj.tra=0.912,RMSEtra=0.477,Q2ext=0.910;SVM:R2adj.tra=0.997,RMSEtra=0.092,Q2ext=0.967)。机理分析表明,温度是影响PL的主要因素,温度越高,蒸气压越大;其次,X1sol也影响PL大小,X1sol用来描述分子间的色散作用,分子间色散力越小,蒸气压越大;此外,化合物的氢键个数、极性和分子构型等因素也影响PL大小。采用Wiliams plot方法表征了PLS模型应用域。所建立的模型可用来预测烷烃、烯烃、醇、酮、羧酸、苯、酚、联苯、卤代芳香烃、含N化合物及含S化合物在不同温度下的PL数据。 相似文献