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Yeon Moon Choo, Deok Jun Jo, Gwan Seon Yun and Eui Hoon Lee
Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). F...
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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Pablo de Llano, Carlos Piñeiro, Manuel Rodríguez
Pág. pp. 163 - 198
This paper offers a comparative analysis of the effectiveness of eight popular forecasting methods: univariate, linear, discriminate and logit regression; recursive partitioning, rough sets, artificial neural networks, and DEA. Our goals are: clarify the...
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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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Devon Barrow, Antonija Mitrovic, Jay Holland, Mohammad Ali and Nikolaos Kourentzes
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide ...
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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Krzysztof Drachal and Michal Pawlowski
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities? prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approa...
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Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance and Timothy J. Pasch
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weathe...
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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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Dibo Dong, Shangwei Wang, Qiaoying Guo, Yiting Ding, Xing Li and Zicheng You
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network?long short-term memory joint fr...
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