IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i19p14222-d1248035.html
   My bibliography  Save this article

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Author

Listed:
  • Hadeel E. Khairan

    (Department of Civil Engineering, Wasit University, Wasit 52001, Iraq)

  • Salah L. Zubaidi

    (Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
    College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq)

  • Syed Fawad Raza

    (College of Earth and Environmental Sciences, University of the Punjab, Lahore 54590, Pakistan)

  • Maysoun Hameed

    (Ministry of Water Resources, Baghdad 10066, Iraq)

  • Nadhir Al-Ansari

    (Department of Civil Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden)

  • Hussein Mohammed Ridha

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Department of Computer Engineering, University of Al-Mustansiriyah, Baghdad 10001, Iraq)

Abstract

Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change.

Suggested Citation

  • Hadeel E. Khairan & Salah L. Zubaidi & Syed Fawad Raza & Maysoun Hameed & Nadhir Al-Ansari & Hussein Mohammed Ridha, 2023. "Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14222-:d:1248035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/19/14222/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/19/14222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
    2. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    3. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    4. Salah K. ElSayed & Ehab E. Elattar, 2021. "Slime Mold Algorithm for Optimal Reactive Power Dispatch Combining with Renewable Energy Sources," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    5. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    6. Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
    7. Qi Ouyang & Wenxi Lu, 2018. "Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 659-674, January.
    8. Milan Gocić & Mohammad Arab Amiri, 2021. "Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1913-1926, April.
    9. Fatemeh Sayyahi & Saeed Farzin & Hojat Karami & Ning Cai, 2021. "Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization," Complexity, Hindawi, vol. 2021, pages 1-12, August.
    10. Hussain, M. Iftikhar & Muscolo, Adele & Farooq, Muhammad & Ahmad, Waqar, 2019. "Sustainable use and management of non-conventional water resources for rehabilitation of marginal lands in arid and semiarid environments," Agricultural Water Management, Elsevier, vol. 221(C), pages 462-476.
    11. Olga Bureneva & Nikolay Safyannikov & Zoya Aleksanyan, 2022. "Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
    12. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
    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. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    2. Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    3. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    4. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).
    5. Mojtaba Kadkhodazadeh & Mahdi Valikhan Anaraki & Amirreza Morshed-Bozorgdel & Saeed Farzin, 2022. "A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods," Sustainability, MDPI, vol. 14(5), pages 1-37, February.
    6. Hadeel E. Khairan & Salah L. Zubaidi & Mustafa Al-Mukhtar & Anmar Dulaimi & Hussein Al-Bugharbee & Furat A. Al-Faraj & Hussein Mohammed Ridha, 2023. "Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    7. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    8. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.
    10. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    11. J. M. Aishwarya & R. Vidhya, 2023. "Study on the Efficiency of a Hydroponic Treatment for Removing Organic Loading from Wastewater and Its Application as a Nutrient for the “ Amaranthus campestris ” Plant for Sustainability," Sustainability, MDPI, vol. 15(10), pages 1-13, May.
    12. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    13. Ghalia Saleem Aljeddani, 2022. "Reusing Sewage Effluent in Greening Urban Areas: A Case Study of: Southern Jeddah, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    14. Mohammed Magdy Hamed & Najeebullah Khan & Mohd Khairul Idlan Muhammad & Shamsuddin Shahid, 2022. "Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan," Land, MDPI, vol. 11(12), pages 1-18, November.
    15. Paulescu, Marius & Badescu, Viorel & Budea, Sanda & Dumitrescu, Alexandru, 2022. "Empirical sunshine-based models vs online estimators for solar resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    16. Enrique A. Enríquez-Velásquez & Victor H. Benitez & Sergey G. Obukhov & Luis C. Félix-Herrán & Jorge de-J. Lozoya-Santos, 2020. "Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study," Energies, MDPI, vol. 13(24), pages 1-41, December.
    17. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    18. Jingyi Zhang & Jiaxin Liu & Yaqi Chen & Xiaochun Feng & Zilai Sun, 2021. "Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
    19. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    20. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.

    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:gam:jsusta:v:15:y:2023:i:19:p:14222-:d:1248035. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.