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Viktoriya Tsyganskaya, Sandro Martinis and Philip Marzahn
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation ...
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Igor Paz, Bernard Willinger, Auguste Gires, Bianca Alves de Souza, Laurent Monier, Hervé Cardinal, Bruno Tisserand, Ioulia Tchiguirinskaia and Daniel Schertzer
Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called ?Multi-Hydro? to study small-scale rainfall variab...
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Zuhang Wu, Yun Zhang, Lifeng Zhang, Xiaolong Hao, Hengchi Lei and Hepeng Zheng
In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by...
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Marciel Lohmann,Leonardo José Cordeiro Santos
Alagamentos e inundações são fenômenos recorrentes principalmente nos meses de verão, cujas consequências acarretam diversos transtornos e perdas materiais para a população afetada. O presente artigo propõe determinar por meio de inteligência artificial ...
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José Luis da Silva Pinho,António Pereira,Rolando Faria
Pág. 69 - 82
Os sistemas de previsão e alerta utilizados na gestão de recursos hídricos e operação de sistemas de drenagem tiveram desenvolvimentos significativos nos últimos anos. Esses desenvolvimentos resultaram da disponibilidade de informações meteorológicas em ...
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Jingxin Guan, Jun Huang, Lei Song and Xiaoqiang Lu
To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle ra...
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Futo Ueda, Hiroto Tanouchi, Nobuyuki Egusa and Takuya Yoshihiro
River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological...
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Matthias Arnold and Sina Keller
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Priyank Kalgaonkar and Mohamed El-Sharkawy
Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets....
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Shaoyan Zuo, Dazhi Wang, Xiao Wang, Liujia Suo, Shuaiwu Liu, Yongqing Zhao and Dewang Liu
In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (????
H
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) and wave period (????
T
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) from X-band marine radar images. Since the shore-based radar image in this study is in...
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