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Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

Author

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  • Sri Lakshmi Sesha Vani Jayanthi

    (Department of Civil Engineering, National Institute of Technology Warangal, Warangal 506004, India)

  • Venkata Reddy Keesara

    (Department of Civil Engineering, National Institute of Technology Warangal, Warangal 506004, India)

  • Venkataramana Sridhar

    (Department of Biological Systems Engineering, Virginia Polytechnic Institute of State University, Blacksburg, VA 24061, USA)

Abstract

Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression ( ν -SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R 2 ), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν -SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.

Suggested Citation

  • Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:6974-:d:833270
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    References listed on IDEAS

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    1. Boini Narsimlu & Ashvin Gosain & Baghu Chahar, 2013. "Assessment of Future Climate Change Impacts on Water Resources of Upper Sind River Basin, India Using SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3647-3662, August.
    2. Gassman, Philip W. & Reyes, Manuel R. & Green, Colleen H. & Arnold, Jeffrey G., 2007. "The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions," ISU General Staff Papers 200701010800001027, Iowa State University, Department of Economics.
    3. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    4. Bhumika Uniyal & Madan Jha & Arbind Verma, 2015. "Assessing Climate Change Impact on Water Balance Components of a River Basin Using SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4767-4785, October.
    5. Jew Das & Alin Treesa & N. V. Umamahesh, 2018. "Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4833-4852, December.
    6. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    7. Jew Das & N. V. Umamahesh, 2018. "Assessment of uncertainty in estimating future flood return levels under climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 109-124, August.
    8. Ruqayah Mohammed & Miklas Scholz, 2017. "Adaptation Strategy to Mitigate the Impact of Climate Change on Water Resources in Arid and Semi-Arid Regions: a Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3557-3573, September.
    9. Koppuravuri Ramabrahmam & Venkata Reddy Keesara & Raghavan Srinivasan & Deva Pratap & Venkataramana Sridhar, 2021. "Flow Simulation and Storage Assessment in an Ungauged Irrigation Tank Cascade System Using the SWAT Model," Sustainability, MDPI, vol. 13(23), pages 1-18, November.
    10. Kisi, Ozgur & Shiri, Jalal & Karimi, Sepideh & Shamshirband, Shahaboddin & Motamedi, Shervin & Petković, Dalibor & Hashim, Roslan, 2015. "A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 731-743.
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