Application of support vector regression for modeling low flow time series
Open accessAbstract
Hydrologic time series modeling using historical records plays a crucial role in forecasting different hydrological processes. The objective of this study is to analyze the suitability of Support Vector Regression (SVR) for modeling monthly low flows time series for three stations in Mahanadi river basin, India. The ‘low flow’ threshold was taken as the Q 75 discharge, i.e., the flow is equal to or surpassed for the duration of 75% of the observation period which was obtained from the daily discharge data. The potential applicability of SVR model is assessed with two different framework models (ANN-ELM, GPR) based on various statistical measures ( r 2 , RMSE, MAE, Nash-Sutcliffe coefficient, objective function (OBJ), Scatter Index (SI) and BIAS). The model selection was based on lowest OBJ value for each station amongst three models (SVR, ANN-ELM, GPR). The SVR model was trained using the Radial Basis Function (RBF). Using the same inputs, the other two models (ANN-ELM and GPR) was also tested. From results, among all the stations, the SVR outperformed GPR and ANN-ELM with lowest OBJ value for the three stations a (1.378, 1.202, 1.570). In addition, the accuracy of the three models were checked using mean forecasting error which were (0.474, 0.421, 0.509) for SVR, (0.507, 0.489 0.500) for GPR and (0.564, 0.603, 0.772) ANN-ELM for the three stations. The results confirm that SVR can be used satisfactorily for modeling monthly low flows in the Mahanadi river basin, India. Hence, the SVR model could be employed as a new reliable and accurate data intelligent approach for predicting the ‘low flow’ ( Q 75 discharge) based on precedent data in water resources and its allied field.
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Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS.
“Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS. Key studies in this area include those by Mehr et al. [180], Oyebode and Stretch [90], Sahoo et al. [181], and Valizadeh et al. [182].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [181]
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- Authors
- Sahoo, B. B., Jha, R., Singh, A., & Kumar, D.
- Affiliations
- National Institute of Technology Patna (IN)
- Corresponding author
- Sahoo, Bibhuti Bhusan
- Venue
- KSCE Journal of Civil Engineering
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 23
- Issue
- 2
- Pages
- 923-934
- ISSN
- 1226-7988 (ISSN-L)
- OpenAlex
- W2905061224
- Cited by
- 56
- References
- 83
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 10 · 2023: 9 · 2022: 13 · 2021: 4 · 2020: 8 · 2019: 2
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- KSCE Journal of Civil Engineering (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Engineering › Ocean Engineering › Water resources management and optimization
- Keywords
- support vector regression, low flow, Mahanadi basin, Gaussian kernel, Gaussian process regression, low flow forecasting
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