Abstract

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.

Highlights

  • The central part of River Nestos flow is one of the areas with the most intensive anthropogenic interference because of the construction and operation of Thesaurus and Platanovrissi dams

  • This study aims to evaluate the predictive capability of ARIMA, Transfer Function (TF) and Artificial Neural Networks (ANN) models for short‐term prediction and for 1, 2 and 3 steps forward (m = 1, 2 and 3)

  • An ARIMA model, a neural network model and a TF model were adapted to the dissolved oxygen time series [12]

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Summary

Introduction

The central part of River Nestos flow is one of the areas with the most intensive anthropogenic interference because of the construction and operation of Thesaurus and Platanovrissi dams. In the deeper section of the reservoir basin, upstream of the Thesaurus dam, a floating telemetric station (Figure 1) was anchored to record changes in water temperature (Tw) and dissolved oxygen (DO) at four different depths (1, 20, 40 and 70 m). Proceedings 2018, 2, 634 records of the years 2004 to 2007 were analyzed and evaluated [3,4,5,6]. This research was completed by the end of the year 2007.

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