The Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece
Antonis Sentas,
Lina Karamoutsou,
Nikos Charizopoulos,
Thomas Psilovikos,
Aris Psilovikos,
Athanasios Loukas
Affiliations
Antonis Sentas
Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Fytoko st., 38446 N. Ionia Magnisias, Greece
Lina Karamoutsou
Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Fytoko st., 38446 N. Ionia Magnisias, Greece
Nikos Charizopoulos
Laboratory of Minearology-Geology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
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
Aris Psilovikos
Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Fytoko st., 38446 N. Ionia Magnisias, Greece
Athanasios Loukas
Department of Civil Engineering, School of Engineering, University of Thessaly, Pedion Areos, 38221 Volos, 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.
