Streamflow prediction using support vector regression machine learning model for Tehri Dam
Abstract Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time seri...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-024-02135-0 |
| _version_ | 1797209129516793856 |
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| author | Bhanu Sharma N. K. Goel |
| author_facet | Bhanu Sharma N. K. Goel |
| author_sort | Bhanu Sharma |
| collection | DOAJ |
| description | Abstract Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R 2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model. |
| first_indexed | 2024-04-24T09:49:48Z |
| format | Article |
| id | doaj.art-4eef814097404556a41b4c835ee7d9b6 |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-04-24T09:49:48Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-4eef814097404556a41b4c835ee7d9b62024-04-14T11:24:19ZengSpringerOpenApplied Water Science2190-54872190-54952024-04-0114512010.1007/s13201-024-02135-0Streamflow prediction using support vector regression machine learning model for Tehri DamBhanu Sharma0N. K. Goel1Department of Hydrology, IIT RoorkeeDepartment of Hydrology, IIT RoorkeeAbstract Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R 2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.https://doi.org/10.1007/s13201-024-02135-0Machine learningOptimizationSupport vector regressionCalibration periodValidation period |
| spellingShingle | Bhanu Sharma N. K. Goel Streamflow prediction using support vector regression machine learning model for Tehri Dam Applied Water Science Machine learning Optimization Support vector regression Calibration period Validation period |
| title | Streamflow prediction using support vector regression machine learning model for Tehri Dam |
| title_full | Streamflow prediction using support vector regression machine learning model for Tehri Dam |
| title_fullStr | Streamflow prediction using support vector regression machine learning model for Tehri Dam |
| title_full_unstemmed | Streamflow prediction using support vector regression machine learning model for Tehri Dam |
| title_short | Streamflow prediction using support vector regression machine learning model for Tehri Dam |
| title_sort | streamflow prediction using support vector regression machine learning model for tehri dam |
| topic | Machine learning Optimization Support vector regression Calibration period Validation period |
| url | https://doi.org/10.1007/s13201-024-02135-0 |
| work_keys_str_mv | AT bhanusharma streamflowpredictionusingsupportvectorregressionmachinelearningmodelfortehridam AT nkgoel streamflowpredictionusingsupportvectorregressionmachinelearningmodelfortehridam |