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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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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