Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow Article Swipe
Jakkarin Weekaew
,
Pakorn Ditthakit
,
Quoc Bao Pham
,
Nichnan Kittiphattanabawon
,
Nguyễn Thị Thùy Linh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/w14244029
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/w14244029
Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy of reservoir inflow forecasting is one of the essential factors supporting reservoir operations. This study aimed to investigate coupling models of feature selection (FS) and machine learning (ML) algorithms to predict the monthly reservoir inflow. The study was carried out using data from the Huai Nam Sai reservoir in southern Thailand. Eighteen years of monthly recorded data (i.e., reservoir inflow, reservoir storage, rainfall, and regional climate indices) with up to a 12-month time lag were utilized. Three ML techniques, i.e., multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN)were compared in their capabilities. In addition, two FS techniques, i.e., genetic algorithm (GA) and backward elimination (BE) methods, were studied with four predictable time intervals, consisting of 3, 6, 9, and 12 months in advance. Ten-fold cross-validation was used for model evaluation. Study results revealed that FS methods (i.e., GA and BE) Could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. BE-ANN provided the best performance for three-time-ahead (T + 3) and nine-time-ahead (T + 9) by giving an OI of 0.9885 and 0.8818, NSE of 0.9546 and 0.9815, RMSE of 1.3155 and 1.2172 MCM/month, MAE of 0.9568 and 0.9644 MCM/month, and r of 0.9796 and 0.9804, respectively. The GA-ANN model showed the highest prediction accuracy for six-time-ahead (T + 6), with an OI of 0.8997, NSE of 0.9407, RMSE of 2.1699 MCM/month, MAE of 1.7549 MCM/month, and r of 0.9759. The ANN model showed the best prediction accuracy for twelve-time-ahead (T + 12), with an OI of 0.9515, NSE of 0.9835, RMSE of 1.1613 MCM/month, MAE of 0.9273 MCM/month, and r of 0.9835.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w14244029
- https://www.mdpi.com/2073-4441/14/24/4029/pdf?version=1671007333
- OA Status
- gold
- Cited By
- 9
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312126451
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312126451Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/w14244029Digital Object Identifier
- Title
-
Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir InflowWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-09Full publication date if available
- Authors
-
Jakkarin Weekaew, Pakorn Ditthakit, Quoc Bao Pham, Nichnan Kittiphattanabawon, Nguyễn Thị Thùy LinhList of authors in order
- Landing page
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https://doi.org/10.3390/w14244029Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-4441/14/24/4029/pdf?version=1671007333Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4441/14/24/4029/pdf?version=1671007333Direct OA link when available
- Concepts
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Inflow, Feature selection, Support vector machine, Artificial neural network, Feature (linguistics), Regression, Machine learning, Environmental science, Computer science, Artificial intelligence, Statistics, Meteorology, Mathematics, Geography, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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