University of Oulu

Danandeh Mehr, A., Ghadimi, S., Marttila, H. et al. A new evolutionary time series model for streamflow forecasting in boreal lake-river systems. Theor Appl Climatol 148, 255–268 (2022).

A new evolutionary time series model for streamflow forecasting in boreal lake-river systems

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Author: Mehr, Ali Danandeh1,2; Ghadimi, Sahand1; Marttila, Hannu1;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, FI90014, Oulu, Finland
2Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.7 MB)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2022-05-06


Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.

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Series: Theoretical and applied climatology
ISSN: 0177-798X
ISSN-E: 1434-4483
ISSN-L: 0177-798X
Volume: 148
Pages: 255 - 268
DOI: 10.1007/s00704-022-03939-3
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Funding: Open Access funding provided by University of Oulu including Oulu University Hospital. The study was supported by the Hydro-RDI project funded by the Academy of Finland (decision number: 337523) and Maa- ja vesitekniikan tuki ry (Reference Number: 41878).
Academy of Finland Grant Number: 337523
Detailed Information: 337523 (Academy of Finland Funding decision)
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