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111.
Zhongyao Liang Yaoyang Xu Gang Zhao Wentao Lu Zhenghui Fu Shuhang Wang Tyler Wagner 《Frontiers of Environmental Science & Engineering》2023,17(6):76
112.
Tienan Ju Mei Lei Guanghui Guo Jinglun Xi Yang Zhang Yuan Xu Qijia Lou 《Frontiers of Environmental Science & Engineering》2023,17(1):8
113.
为研究城市地铁沿线老旧房屋普遍存在结构安全问题,基于机器学习模型,选取房屋年份、楼层、面积等11个属性构建预警指标体系,采用SMOTE过采样、独热编码等方法解决样本离散化、不均衡的问题;利用KNN、Bayes、Logistic、SVM 4种机器学习模型对房屋结构安全数据学习并测试,综合应用Accuracy、F1、AP、... 相似文献
114.
基于BN的FTA在通用航空风险评价中的应用 总被引:1,自引:1,他引:0
针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。 相似文献
115.
目前,航空器看错、落错跑道事件在国际上是一个相当突出的安全问题,这个问题在我国近年也突显出来。导致航空器看错落错跑道的原因十分复杂,且此类事件及易引发严重的后果,造成人、财、物等多方面的损失,本文特对此类问题开展了一系列的研究。为了预防航空器看错、落错跑道事故的发生,查找诱发该类事故的因素,针对航空器看错、落错跑道事故的形成特点,笔者运用贝叶斯网络探讨其各影响因素间的关系和相互作用,该网络强大的逻辑推理能力,克服了不完备样本空间带来的不足,揭示了人、机、环境与管理因素相互作用的内在规律。为改善机场安全管理的科学性、可靠性,进一步降低事故率,提供了可靠的技术支持。 相似文献
116.
为了更好地反映环境污染变化趋势,为环境管理决策提供及时、全面的环境质量信息,预防严重污染事件发生,开展城市空气质量预报研究是十分必要的.本文针对环境大数据时代下的城市空气质量预报,提出了一种基于深度学习的新方法.该方法通过模拟人类大脑的神经连接结构,将数据在原空间的特征表示转换到具有语义特征的新特征空间,自动地学习得到层次化的特征表示,从而提高预报性能.得益于这种方式,新方法与传统方法相比,不仅可以利用空气质量监测、气象监测及预报等环境大数据,充分考虑污染物的时空变化、空间分布,得到语义性的污染物变化规律,还可以基于其他空气污染预测方法的结果(如数值预报模式),自动分析其适用范围、优势劣势.因此,新方法通过模拟人脑思考过程实现更充分的大数据集成,一定程度上克服了现有方法的缺陷,应用上更加具有灵活性和可操作性.最后,通过实验证明新方法可以提高空气污染预报性能. 相似文献
117.
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
118.
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
119.
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
120.
基于贝叶斯网络的基本农田划定方法 总被引:3,自引:0,他引:3
基本农田划定是落实耕地保护、保证粮食安全的关键问题。目前,基本农田划定的相关研究多基于对耕地适宜性条件的评价,而未涉及对耕地历史变化过程的考虑,因而不能保证划定结果适应实际区域的耕地变化趋势,导致基本农田易被占用,补划与调整频繁。论文综合考虑耕地适宜性条件与历史动态变化状况,提出了一种基于贝叶斯网络模型的基本农田划定方法。以湖北省大冶市这一典型资源型城市为实验区进行了实例研究,划定结果表明,该划定方法能在保证基本农田划定数量与质量的同时,提高其农业用途的稳定性,促进基本农田保护与经济建设、生态保护间协调发展,是一种有效的划定模型。 相似文献