Abstract
River flow modeling is essential for critical aspects such as effective water management and structure planning, together with flood and drought analysis. There has been a growing interest in modeling hydrological systems via machine learning (ML) models. Various optimization techniques are utilized to develop the applications of ML-based hydrological models. The ultimate aim of this research is to establish high performance forecasting model. Therefore, this study conducts river flow modeling by using the daily data attained from a gauge station situated in the Euphrates Basin. For this purpose, Artificial Neural Network (ANN) model was hybridized with five different optimization algorithms i.e., Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO), Ant Colony Optimization (ACO), Ant-Lion Optimization (ALO), and Imperialist Competitive Algorithm (ICA). In determining the inputs used to create the models, the distribution graph and correlation of the data with the previous period data were examined. The results were evaluated with eleven different statistical parameters and an error matrix. Examining the obtained results, the study revealed all models present high performance. When the results were reviewed in general, it was seen that all determination coefficient (R2) and Nash-Sutcliffe coefficient (NSE) values were higher than 0.962, and other parameters were very close to the optimum. By comparing all the developed hybrid models, ANN-ALO model reported the highest performance for river flow forecasting.
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Data Availability
The daily river flow data used in the study can be accessed through the Republic of Turkey State Hydraulic Works Flow Observation Annuals. Annuals are available online at https://www.dsi.gov.tr/Sayfa/Detay/744.Reviewer
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Pijush Samui], [Zaher Mundher Yaseen], [Sanjiban Sekhar Roy] and [Sanjay Kumar]. The first draft of the manuscript was written by [Sefa Nur Yesilyurt] and [Huseyin Yildirim Dalkilic]. All authors read and approved the final manuscript.
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Communicated by: H. Babaie
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Samui, P., Yesilyurt, S.N., Dalkilic, H.Y. et al. Comparison of different optimized machine learning algorithms for daily river flow forecasting. Earth Sci Inform 16, 533–548 (2023). https://doi.org/10.1007/s12145-022-00896-3
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DOI: https://doi.org/10.1007/s12145-022-00896-3