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Zhaoxin Wang, Tiejun Wang and Yonggen Zhang
Knowledge of both state (e.g., soil moisture) and flux (e.g., actual evapotranspiration (ETa) and groundwater recharge (GR)) hydrological variables across vadose zones is critical for understanding ecohydrological and land-surface processes. In this stud...
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Zeyu Xu, Wenbin Yu, Chengjun Zhang and Yadang Chen
In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM)...
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Livinia Saputra, Sang Hyun Kim, Kyung-Jin Lee, Seo Jin Ki, Ho Young Jo, Seunghak Lee and Jaeshik Chung
The vadose zone acts as a natural buffer against groundwater contamination, and thus, its attenuation capacity (AC) directly affects groundwater vulnerability to pollutants. A regression model from the previous study predicting the overall AC of soils ag...
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Yafei Xi, Quanhua Hou, Yaqiong Duan, Kexin Lei, Yan Wu and Qianyu Cheng
Exploring the correlation of the built environment with metro ridership is vital for fostering sustainable urban growth. Although the research conducted in the past has explored how ridership is nonlinearly influenced by the built environment, less resea...
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Tingting Fan, Yuchen Wang, Zhiguo Xu, Lining Sun, Peitao Wang and Jingming Hou
Tsunami monitoring and early warning systems are mainly established to deal with seismogenic tsunamis generated by sudden seafloor fault displacement. However, a global tsunami triggered by the 2022 Tonga volcanic eruption promoted the need for tsunami e...
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Arghadyuti Banerjee, Leo Creedon, Noelle Jones, Laurence Gill and Salem Gharbia
Assuring the quantity and quality of groundwater resources is essential for the well-being of human and ecological health, society, and the economy. For the last few decades, groundwater vulnerability modeling techniques have become essential for groundw...
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Benjamin Burrichter, Julian Hofmann, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview ...
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Chen Deng, Chengqi Cheng, Tengteng Qu, Shuang Li and Bo Chen
With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access wi...
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Shukai Li, Xiaofang Wang, Dongri Shan and Peng Zhang
Temporal modeling is a key problem in action recognition, and it remains difficult to accurately model temporal information of videos. In this paper, we present a local spatiotemporal extraction module (LSTE) and a channel time excitation module (CTE), w...
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Qianlong Jin, Yu Tian, Weicong Zhan, Qiming Sang, Jiancheng Yu and Xiaohui Wang
Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-tim...
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