IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v13y1999i3p219-231.html
   My bibliography  Save this article

A Genetic Programming Approach to Rainfall-Runoff Modelling

Author

Listed:
  • Dragan Savic
  • Godfrey Walters
  • James Davidson

Abstract

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a ‘transparent’ and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    • Handle: RePEc:spr:waterr:v:13:y:1999:i:3:p:219-231
    DOI: 10.1023/A:1008132509589
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1008132509589
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1008132509589?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. K. Aziz & Sohail Rai & A. Rahman, 2015. "Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia," 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. 77(2), pages 805-821, June.
    2. Y. Yang & Patrick Ray & Casey Brown & Abedalrazq Khalil & Winston Yu, 2015. "Estimation of flood damage functions for river basin planning: a case study in Bangladesh," 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. 75(3), pages 2773-2791, February.
    3. M. G. Erechtchoukova & P. A. Khaiter & S. Saffarpour, 2016. "Short-Term Predictions of Hydrological Events on an Urbanized Watershed Using Supervised Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4329-4343, September.
    4. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
    5. Elahe Fallah-Mehdipour & Omid Bozorg Haddad & Saeed Alimohammadi & Hugo Loáiciga, 2015. "Development of Real-Time Conjunctive Use Operation Rules for Aquifer-Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1887-1906, April.
    6. Abinash Mohanta & K. C. Patra & Arpan Pradhan, 2020. "Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1047-1073, February.
    7. Vincent Wolfs & Patrick Willems, 2017. "Modular Conceptual Modelling Approach and Software for Sewer Hydraulic Computations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 283-298, January.
    8. Madan Jha & Gaurav Nanda & Manoj Samuel, 2004. "Determining Hydraulic Characteristics of Production Wells using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(4), pages 353-377, August.
    9. Aytac Guven & Özgür Kişi, 2011. "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 691-704, January.
    10. Xiangwei Wang & Yizhe Yang & Jianglong Lv & Hailong He, 2023. "Past, present and future of the applications of machine learning in soil science and hydrology," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(2), pages 67-80.
    11. Ali Arefinia & Omid Bozorg-Haddad & Khaled Ahmadaali & Javad Bazrafshan & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8378-8396, June.
    12. Rajib Bhattacharjya & Sandeep Chaurasia, 2013. "Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 567-579, January.
    13. E. Fallah-Mehdipour & O. Bozorg Haddad & H. Orouji & M. Mariño, 2013. "Application of Genetic Programming in Stage Hydrograph Routing of Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3261-3272, July.
    14. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    15. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    16. Alireza B. Dariane & M. M. Javadianzadeh & L. Douglas James, 2016. "Developing an Efficient Auto-Calibration Algorithm for HEC-HMS Program," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 1923-1937, April.

    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:waterr:v:13:y:1999:i:3:p:219-231. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.