Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e ar...The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.展开更多
延迟是全球卫星导航定位中重要的误差源之一,提高电离层TEC建模和预报精度对改善卫星导航定位精度至关重要.本文构建了以太阳辐射通量指数F_(10.7)、地磁活动指数Dst、地理坐标和中国科学院(Chinese Academy of Sciences,CAS)GIM数据为...延迟是全球卫星导航定位中重要的误差源之一,提高电离层TEC建模和预报精度对改善卫星导航定位精度至关重要.本文构建了以太阳辐射通量指数F_(10.7)、地磁活动指数Dst、地理坐标和中国科学院(Chinese Academy of Sciences,CAS)GIM数据为输入参数的NeuralProphet神经网络模型(NP模型),实现在2015年3月特大磁暴期中国区域电离层TEC短期预报.为验证NP模型的预报精度,本文同时构建了长短期记忆神经网络(Long Short-term Memory Neural Network,LSTM)模型进行对比分析.结果统计分析表明,NP模型在磁暴期(2015年DOY076-078)TEC预报值RMSE和RD分别为0.83 TECU和3.13%,绝对和相对精度较LSTM模型分别提高1.49 TECU和10.25%;且NP模型RMSE优于1.5 TECU的比例达97.24%,远高于LSTM模型.NP模型预报值与CAS具有较好一致性和无偏性,偏差均值仅为-0.01 TECU,而LSTM模型预报值的均值偏大,偏差均值为1.49 TECU.从低纬到中纬度的三个纬度带内,NP模型RMSE分别为1.12、0.83和0.44 TECU,精度比LSTM模型提高1.94、1.56和1.23 TECU.整体上,在磁暴期NP模型预报性能明显优于LSTM模型,能够精细描述中国区域电离层TEC时空变化.展开更多
经常发生的地磁暴可引起电离层异常,并导致穿过电离层的GNSS导航信号产生异常延迟甚至难以被观测处理。因此,有必要对地球磁暴引起的电离层异常响应特征开展系统深入研究。在已有的全球电离层异常研究基础上,充分发挥了省级连续运行参考...经常发生的地磁暴可引起电离层异常,并导致穿过电离层的GNSS导航信号产生异常延迟甚至难以被观测处理。因此,有必要对地球磁暴引起的电离层异常响应特征开展系统深入研究。在已有的全球电离层异常研究基础上,充分发挥了省级连续运行参考站(Continuous Operation Reference Station,CORS)网测站密度大、数据细节丰富的优势,建立了区域电离层模型,精细化提取了电离层异常值。初步分析了磁暴期间电离层异常响应的时序关系、量级大小、空间分布和变化规律等:(1)磁暴与区域电离层异常之间的时间响应特征显示,地球磁暴可引起电离层异常,电离层异常在响应时间方面具有拖尾效应,磁暴结束24 h后电离层才恢复至磁暴前正常水平。(2)磁暴引起电离层垂直电子总含量(Vertical Total Electron Content,VTEC)异常变化的量级特征显示,小磁暴引起电离层天顶方向电子总量增大约9.5 TECU,对应视线方向电子总量增大约36 TECU。(3)磁暴引起电离层异常的空间分布特征显示,高纬度地区的电离层异常响应大于低纬度地区。(4)电离层异常响应的空间变化特征显示,磁暴期间电离层异常响应首先呈现出从南向北增大延伸态势;当电离层VTEC及其异常值达到峰值后,电离层异常响应呈现从北向南减弱回归态势。展开更多
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
文摘The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.
文摘经常发生的地磁暴可引起电离层异常,并导致穿过电离层的GNSS导航信号产生异常延迟甚至难以被观测处理。因此,有必要对地球磁暴引起的电离层异常响应特征开展系统深入研究。在已有的全球电离层异常研究基础上,充分发挥了省级连续运行参考站(Continuous Operation Reference Station,CORS)网测站密度大、数据细节丰富的优势,建立了区域电离层模型,精细化提取了电离层异常值。初步分析了磁暴期间电离层异常响应的时序关系、量级大小、空间分布和变化规律等:(1)磁暴与区域电离层异常之间的时间响应特征显示,地球磁暴可引起电离层异常,电离层异常在响应时间方面具有拖尾效应,磁暴结束24 h后电离层才恢复至磁暴前正常水平。(2)磁暴引起电离层垂直电子总含量(Vertical Total Electron Content,VTEC)异常变化的量级特征显示,小磁暴引起电离层天顶方向电子总量增大约9.5 TECU,对应视线方向电子总量增大约36 TECU。(3)磁暴引起电离层异常的空间分布特征显示,高纬度地区的电离层异常响应大于低纬度地区。(4)电离层异常响应的空间变化特征显示,磁暴期间电离层异常响应首先呈现出从南向北增大延伸态势;当电离层VTEC及其异常值达到峰值后,电离层异常响应呈现从北向南减弱回归态势。
