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Luis Zuloaga-Rotta, Rubén Borja-Rosales, Mirko Jerber Rodríguez Mallma, David Mauricio and Nelson Maculan
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in ...
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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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Chi Han, Wei Xiong and Ronghuan Yu
Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of m...
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Qian Liu, Xiaofeng Zhao, Jing Zou, Yunzhou Li, Zhijin Qiu, Tong Hu, Bo Wang and Zhiqian Li
The Coupled Ocean?Atmosphere?Wave?Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South C...
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Anika Stelzl and Daniela Fuchs-Hanusch
Austria?s water utilities are facing new challenges due to advancing climate change. In recent years, changes in water demand have been observed. Water demand forecast models are required to assess these changes and react to them in a sustainable way. In...
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