IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i4d10.1007_s11269-022-03068-6.html
   My bibliography  Save this article

Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Author

Listed:
  • Icen Yoosefdoost

    (University of Birjand)

  • Abbas Khashei-Siuki

    (University of Birjand)

  • Hossein Tabari

    (KU Leuven)

  • Omolbani Mohammadrezapour

    (Gorgan University of Agriculture Science and Natural Resources)

Abstract

Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations more challenging. This study aims to simulate input runoff to a dam reservoir in an arid region under changing climatic conditions using three data-mining algorithms, including Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Genetic Expression Programming (GEP), and the conceptual HYMOD model. Three parameters containing precipitation and maximum and minimum temperature were simulated from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) and Global Climate Models (GCMs) for the future period (2020–2040) under the high-end RCP8.5 scenario. The Long Ashton Research Station Weather Generator (LARS-WG) was selected as a downscaling method. The Gamma and M tests (This is an exam to determine whether an infinite series of functions will converge uniformly and absolutely or not) were applied to detect the best combinations and number of input parameters for the models, respectively. Among 29 defined input parameters for the models, the gamma test identified 11 parameters with the best functionality to simulate runoff. Based on the reliability estimates of model error variance by the M test, the data were partitioned as 75% for learning and the other 25% for test verification. A comparison of the runoff simulations of the models revealed a remarkable performance of the SVM model by 3, 5, and 14% compared to ANNs, GEP, and HYMOD models, respectively. The SVM model forecasted a 25% decrease in the mean runoff input to the dam reservoir for the 2020–2040 period compared to the study period (2000–2019). This result illustrates necessitating the implementation of sustainable adaptation strategies to protect future water resources in the basin.

Suggested Citation

  • Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03068-6
    DOI: 10.1007/s11269-022-03068-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03068-6
    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/s11269-022-03068-6?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. Abinash Mohanta & Arpan Pradhan & Monalisa Mallick & K. C. Patra, 2021. "Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4535-4559, October.
    2. S. Vijay & K. Kamaraj, 2021. "Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 535-553, January.
    3. Yaser Amiri-Ardakani & Mohammad Najafzadeh, 2021. "Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3703-3720, September.
    4. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    5. Rabin Chakrabortty & Subodh Chandra Pal & Saeid Janizadeh & M. Santosh & Paramita Roy & Indrajit Chowdhuri & Asish Saha, 2021. "Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4251-4274, September.
    6. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
    7. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
    8. Ahmad Jafarzadeh & Mohsen Pourreza-Bilondi & Abbas Khashei Siuki & Javad Ramezani Moghadam, 2021. "Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 407-427, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Xiao Li & Liping Zhang & Sidong Zeng & Zhenyu Tang & Lina Liu & Qin Zhang & Zhengyang Tang & Xiaojun Hua, 2022. "Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    2. Pablo F. Andreoni & Marcia A. Ruiz & María Inés Rodríguez & Ana Laura Ruibal-Conti, 2022. "Unraveling the Lagged Effect of Hydro-meteorological Conditions On the Trophic State of a Reservoir By Applying Dynamic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4275-4291, September.
    3. Pedram Pishgah Hadiyan & Ramtin Moeini & Eghbal Ehsanzadeh & Monire Karvanpour, 2022. "Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2703-2723, June.

    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. Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
    2. C, Deep Prakash & Majumdar, Adrija, 2023. "Predicting sports fans’ engagement with culturally aligned social media content: A language expectancy perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    4. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    5. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    6. Md. Uzzal Mia & Tahmida Naher Chowdhury & Rabin Chakrabortty & Subodh Chandra Pal & Mohammad Khalid Al-Sadoon & Romulus Costache & Abu Reza Md. Towfiqul Islam, 2023. "Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer," Land, MDPI, vol. 12(4), pages 1-26, April.
    7. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
    8. Arshia Amiri & Ulf-G Gerdtham, 2012. "Granger Causality Between Exports, Imports and GDP in France: Evidance from Using Geostatistical Models," Economic Research Guardian, Weissberg Publishing, vol. 2(1), pages 43-59, May.
    9. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.
    10. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.
    11. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    12. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    13. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    14. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
    15. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
    16. Parvin Golfam & Parisa-Sadat Ashofteh, 2022. "Performance Indexes Analysis of the Reservoir-Hydropower Plant System Affected by Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5127-5162, October.
    17. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    18. Chao Liu & Mingshuang Xu & Yufeng Liu & Xuefei Li & Zonglin Pang & Sheng Miao, 2022. "Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study," IJERPH, MDPI, vol. 19(23), pages 1-14, November.
    19. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
    20. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.

    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:36:y:2022:i:4:d:10.1007_s11269-022-03068-6. 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.