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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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Minghao Liu, Qingxi Luo, Jianxiang Wang, Lingbo Sun, Tingting Xu and Enming Wang
Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth?s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining...
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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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Qingyong Zhang, Lingfeng Zhou, Yixin Su, Huiwen Xia and Bingrong Xu
Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In p...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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Dapeng Jiang, Guoyou Shi, Na Li, Lin Ma, Weifeng Li and Jiahui Shi
In the context of the rapid development of deep learning theory, predicting future motion states based on time series sequence data of ship trajectories can significantly improve the safety of the traffic environment. Considering the spatiotemporal corre...
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Tian Xu and Qingnian Zhang
To analyze the changing characteristics of ship traffic flow in wind farms water area, and to improve the accuracy of ship traffic flow prediction, a Gated Recurrent Unit (GRU) of a Recurrent Neural Network (RNN) was established to analyze multiple traff...
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Jeba Nadarajan and Rathi Sivanraj
Periodic traffic prediction and analysis is essential for urbanisation and intelligent transportation systems (ITS). However, traffic prediction is challenging due to the nonlinear flow of traffic and its interdependencies on spatiotemporal features. Tra...
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Chunlei Mi, Shifen Cheng and Feng Lu
Predicting taxi-calling demands at the urban area level is vital to coordinate the supply?demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose...
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Guie Li, Yangyang Jiao, Jie Li and Qingwu Yan
China has made remarkable reductions in absolute poverty. However, pressing questions remain of how to consolidate the existing achievements of poverty alleviation and prevent rural households from regressing back into poverty, especially in continuously...
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