Ali Danandeh Mehr, Hannu Marttila, Ali Torabi Haghighi, Danny Croghan, Nasrin Fathollahzadeh Attar; GTAR: a new ensemble evolutionary autoregressive approach to model dissolved organic carbon. AQUA - Water Infrastructure, Ecosystems and Society 1 March 2023; 72 (3): 381–394. doi: https://doi.org/10.2166/aqua.2023.235
GTAR : a new ensemble evolutionary autoregressive approach to model dissolved organic carbon
|Author:||Mehr, Ali Danandeh1,2; Marttila, Hannu3; Torabi Haghighi, Ali4;|
1Civil Engineering Department, Antalya Bilim University, Antalya, Turkey
2Faculty of Information Technology, Middle East University, Amman 11831, Jordan
3Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI 90014, Oulu, Finland
4Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI 90014, Oulu, Finlan
5Department of Statistical Sciences, University of Padova, Via Cesare Battisti, Padova 35121, Italy
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230927137590
|Publish Date:|| 2023-09-27
This article explores the forecasting capabilities of three classic linear and nonlinear autoregressive modeling techniques and proposes a new ensemble evolutionary time series approach to model and forecast daily dynamics in stream dissolved organic carbon (DOC). The model used data from the Oulankajoki River basin, a boreal catchment in Northern Finland. The models that were evolved used both accuracy and parsimony measures including autoregressive (AR), vector autoregressive (VAR), and self-exciting threshold autoregressive (SETAR). The new method, called genetic-based SETAR (GTAR), evolved through the integration of state-of-the-art genetic programming with SETAR. To develop the models, high-resolution DOC concentration and daily streamflow (as the external input for VAR) were measured at the same gauging station throughout the ice free season. The results showed that all the models characterize the DOC dynamics with an acceptable 1-day-ahead forecasting accuracy. Use of the streamflow time series as an exogenous variable did not increase the predictive accuracy of AR models. Moreover, the hybrid GTAR provided the best accuracy for the holdout testing data and proved to be a suitable approach for predicting DOC in boreal conditions.
|Pages:||381 - 394|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
This research was funded by HYDRO-RDI network, HYDRO-RI-platform and Green-Digi-Basin, Academy of Finland (337523, 346163, 347704) and Freshwater Competence Centre (FWCC).
|Academy of Finland Grant Number:||
337523 (Academy of Finland Funding decision)
346163 (Academy of Finland Funding decision)
347704 (Academy of Finland Funding decision)
© 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).