Athanasios Loukas
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Athanasios Loukas has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Antonis Sentas
Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Fytoko st., 38446 N. Ionia Magnisias, Greece
1 shared publication - Aris Psilovikos
Laboratory of Ecohydraulics and Inland Water Management, Department of Ichthyology and Aquatic Environment, University of Thessaly, Fytokou Street, 38446 New Ionia, Greece
1 shared publication - Lina Karamoutsou
Laboratory of Ecohydraulics & Inland Water Management, Department of Ichthyology and Aquatic Environment, University of Thessaly, 38446 Volos, Greece
1 shared publication - Nikos Charizopoulos
Laboratory of Mineralogy-Geology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
1 shared publication - Thomas Psilovikos
Department of Forest and Water engineering, Laboratory of Mechanical Science and Topography, School of Forestry & Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
1 shared publication
Latest Publications
- Open Access
- 30 July 2018
- Published by MDPI AG in Proceedings
- Vol. 2 (11) , 634
The Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece
The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.
- 30 July 2018
- Published by MDPI AG in Proceedings
- Vol. 2 (11) , 634
Open AccessThe Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece
The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.