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Cynthia Andraos
The expected change in rainfall patterns and the increase in evapotranspiration due to climate change leads to earlier droughts, which aggravate water shortages. To ensure the sustainable management of water resources in these conditions, it is necessary...
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Costas Varotsos, Nicholas V. Sarlis, Yuri Mazei, Damir Saldaev and Maria Efstathiou
Remotely sensed data play a crucial role in monitoring the El Niño/La Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate...
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Marco-Michael Temme, Olga Gluchshenko, Lennard Nöhren, Matthias Kleinert, Oliver Ohneiser, Kathleen Muth, Heiko Ehr, Niklas Groß, Annette Temme, Martina Lagasio, Massimo Milelli, Vincenzo Mazzarella, Antonio Parodi, Eugenio Realini, Stefano Federico, Rosa Claudia Torcasio, Markus Kerschbaum, Laura Esbrí, Maria Carmen Llasat, Tomeu Rigo and Riccardo Biondiadd Show full author list remove Hide full author list
In the H2020 project ?Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM? (SINOPTICA), an air traffic controller support system was extended to organize approaching traffic even under severe weather conditions. During...
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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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Natalí Carbo-Bustinza, Hasnain Iftikhar, Marisol Belmonte, Rita Jaqueline Cabello-Torres, Alex Rubén Huamán De La Cruz and Javier Linkolk López-Gonzales
In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these ...
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Charalampos M. Liapis and Sotiris Kotsiantis
The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given th...
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Claudio Meneguzzer
Understanding the many facets of repeated route choice behavior in traffic networks is essential for obtaining accurate flow forecasts and enhancing the effectiveness of traffic management measures. This paper presents a model of the day-to-day evolution...
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Youssef Karout, Axel Curcio, Julien Eynard, Stéphane Thil, Sylvain Rodat, Stéphane Abanades, Valéry Vuillerme and Stéphane Grieu
The present paper deals with both the modeling and the dynamic control of a solar hybrid thermochemical reactor designed to produce syngas through the high-temperature steam gasification of biomass. First, a model of the reactor based on the thermodynami...
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Katleho Makatjane and Tshepiso Tsoku
This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which a...
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Christian Ulrich, Benjamin Frieske, Stephan A. Schmid and Horst E. Friedrich
Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of th...
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