IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i7d10.1007_s10668-023-03412-9.html
   My bibliography  Save this article

Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches

Author

Listed:
  • Sandeep Samantaray

    (NIT Silchar)

  • Abinash Sahoo

    (NIT Silchar
    OUTR)

Abstract

Accurate monthly flow discharge prediction can yield significant evidence for sustainable management of water resources systems, optimal water allocation and use, mitigating flood events, and warning against famine. Inspiration to explore and develop skillful prediction models is a continuing attempt for various hydrologic assessments. The main aim of present study is to explore the potential of novel hybrid PSR-SVM-FFA model (integration of phase space reconstruction with support vector machine and firefly algorithm) and assess its performance against conventional radial basis function network, SVM, and hybrid SVM-FFA to predict flow discharge considering data from four gauge stations of Mahanadi River basin, India. PSR is applied to extract information and characteristics from flow time series and improve accuracy of hybrid SVM-FFA model. For assessing the model’s enactment, Nash–Sutcliffe coefficient root-mean-square error and Willmott’s Index (WI) indicators are calculated. The results showed that PSR-SVM-FFA model generated improved monthly flow predictions than other applied methods. The result indicates that best values of WI are 0.912–0.929, 0.949–0.956, 0.961–0.967, and 0.98–0.984 for RBFN, SVM, SVM-FFA, and PSR-SVM-FFA, respectively. This demonstrates that PSR-SVM-FFA provides prominent predictions compared to the other three approaches.

Suggested Citation

  • Sandeep Samantaray & Abinash Sahoo, 2024. "Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 18699-18723, July.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:7:d:10.1007_s10668-023-03412-9
    DOI: 10.1007/s10668-023-03412-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-03412-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-023-03412-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    2. Honghai Qi & Pu Qi & M. Altinakar, 2013. "GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3631-3645, August.
    3. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    4. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    5. Jianzhu Li & Senming Tan, 2015. "Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5533-5550, December.
    6. Binaya Kumar Panigrahi & Tushar Kumar Nath & Manas Ranjan Senapati, 2019. "An application of local linear radial basis function neural network for flood prediction," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 67-87, January.
    7. Hai Tao & Sadeq Oleiwi Sulaiman & Zaher Mundher Yaseen & H. Asadi & Sarita Gajbhiye Meshram & M. A. Ghorbani, 2018. "What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3935-3959, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong Jiang & Jinlong Gai & Shukuan Zhao & Peggy E. Chaudhry & Sohail S. Chaudhry, 2022. "Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 361-378, May.
    2. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    3. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    4. Jianzhu Li & Qiushuang Ma & Yu Tian & Yuming Lei & Ting Zhang & Ping Feng, 2019. "Flood scaling under nonstationarity in Daqinghe River basin, China," 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. 98(2), pages 675-696, September.
    5. Qi Liao & Ge Yu & Wensheng Jiang & Chunxia Lu & Yan Ma & Kexiu Liu & Qun Lin & Yanping Wang, 2019. "Research on the Risk Assessment of Qingdao Marine Disaster Based on Flooding," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    6. Jianzhu Li & Yuming Lei & Senming Tan & Colin D. Bell & Bernard A. Engel & Yixuan Wang, 2018. "Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4239-4252, October.
    7. Xinyu Wan & Qingyan Yang & Peng Jiang & Ping’an Zhong, 2019. "A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 4027-4050, September.
    8. Chaochao Li & Xiaotao Cheng & Na Li & Xiaohe Du & Qian Yu & Guangyuan Kan, 2016. "A Framework for Flood Risk Analysis and Benefit Assessment of Flood Control Measures in Urban Areas," IJERPH, MDPI, vol. 13(8), pages 1-18, August.
    9. Lin She & Xue-yi You, 2019. "A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3143-3153, July.
    10. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    11. Yiming Hu & Zhongmin Liang & Vijay P. Singh & Xuebin Zhang & Jun Wang & Binquan Li & Huimin Wang, 2018. "Concept of Equivalent Reliability for Estimating the Design Flood under Non-stationary Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 997-1011, February.
    12. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    13. Neeraj Dhanraj Bokde & Zaher Mundher Yaseen & Gorm Bruun Andersen, 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling," Energies, MDPI, vol. 13(10), pages 1-24, May.
    14. Alireza Farrokhi & Saeed Farzin & Sayed-Farhad Mousavi, 2020. "A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3363-3385, August.
    15. Jamei, Mehdi & Karbasi, Masoud & Malik, Anurag & Jamei, Mozhdeh & Kisi, Ozgur & Yaseen, Zaher Mundher, 2022. "Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms," Agricultural Water Management, Elsevier, vol. 269(C).
    16. He, Li & Du, Yu & Yu, Menglong & Wen, Hao & Ma, Haochen & Xu, Ying, 2024. "A stochastic simulation-based method for predicting the carrying capacity of agricultural water resources," Agricultural Water Management, Elsevier, vol. 291(C).
    17. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    18. Chi Zhang & Xuezhi Gu & Lei Ye & Qian Xin & Xiaoyang Li & Hairong Zhang, 2023. "Climate Informed Non-stationary Modeling of Extreme Precipitation in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3319-3341, July.
    19. Guangming Yu & Sa Wang & Qiwu Yu & Lei Wu & Yong Fan & Xiaoli He & Xia Zhou & Huanhuan Jia & Shu Zhang & Xiaojuan Tian, 2014. "The Regional Limit of Flood-Bearing Capability: A Theoretical Model and Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1921-1936, May.
    20. Fatih Dikbaş, 2018. "A New Two-Dimensional Rank Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1539-1553, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:26:y:2024:i:7:d:10.1007_s10668-023-03412-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.