IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v208y2018icp140-151.html
   My bibliography  Save this article

Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso

Author

Listed:
  • Tao, Hai
  • Diop, Lamine
  • Bodian, Ansoumana
  • Djaman, Koffi
  • Ndiaye, Papa Malick
  • Yaseen, Zaher Mundher

Abstract

Reference Evapotranspiration (ETo) is one of the major components of the hydrological cycle that is very essential in water resources planning, irrigation and drainage management and several other hydrology processes. In irrigation system and design, the prediction of ETo is vital and indispensable for the quantification of crop water needs. This study investigates the capabilities of hybridized fuzzy model with firefly algorithm (ANFIS-FA) for predicting daily reference evapotranspiration over Burkina Faso region. Metrological information at Bobo Dioulasso, Bur Dedougou, and Ouahigouya stations, in Sahelian, Sudano-Sahelian, and Sudanian zone, are used for modelling development. Six different climatic input variable combinations corresponding to 6 models are inspected. The daily Penman-Monteith reference evapotranspiration for the time-period (1998–2012) are used to train and test the models. Several numerical indicators in addition to Taylor diagram are considered to evaluate the performance of the models. Results indicated that the hybrid ANFIS-FA model outperformed the classical ANFIS-based model for all three stations and the model with full inputs climatic data gave the best results. Furthermore, ANFIS-FA is performed the best for Bur Dedougou (Sahalian-Soudanian region) and less at Ouahigouya (sahalian region). In quantitative terms and for instance Bur Dedougou station, ANFIS-FA model increased the prediction accuracy remarkably (SI = 0.043, R2 = 0.97, MAPE = 0.035 and RMSE = 0.24) compared with ANFIS-based model (SI = 0.068, R2 = 0.89, MAPE = 0.037 and RMSE = 0.378). Results revealed the influence of utilizing nature-inspired firefly algorithm to substantially improve performance of the classical ANFIS model optimization for the applied application. Also, the applied modelling strategy can bring a trustful expert intelligent system for predicting reference evapotranspiration at the west desert of Africa.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:208:y:2018:i:c:p:140-151
    DOI: 10.1016/j.agwat.2018.06.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377418308151
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2018.06.018?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. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    2. Martin Jung & Markus Reichstein & Philippe Ciais & Sonia I. Seneviratne & Justin Sheffield & Michael L. Goulden & Gordon Bonan & Alessandro Cescatti & Jiquan Chen & Richard de Jeu & A. Johannes Dolman, 2010. "Recent decline in the global land evapotranspiration trend due to limited moisture supply," Nature, Nature, vol. 467(7318), pages 951-954, October.
    3. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    4. Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
    5. Hatice Citakoglu & Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2014. "Estimation of Monthly Mean Reference Evapotranspiration in Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 99-113, 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. 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.
    2. 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).
    3. Kisi, Ozgur & Heddam, Salim & Yaseen, Zaher Mundher, 2019. "The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model," Applied Energy, Elsevier, vol. 241(C), pages 184-195.
    4. Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).
    5. 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).
    6. 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.
    7. Hai Tao & Isa Ebtehaj & Hossein Bonakdari & Salim Heddam & Cyril Voyant & Nadhir Al-Ansari & Ravinesh Deo & Zaher Mundher Yaseen, 2019. "Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme," Energies, MDPI, vol. 12(7), pages 1-24, April.
    8. Habibeh Sharifi & Abbas Roozbahani & Seied Mehdy Hashemy Shahdany, 2021. "Evaluating the Performance of Agricultural Water Distribution Systems Using FIS, ANN and ANFIS Intelligent Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1797-1816, April.
    9. 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).
    10. 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.
    11. 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.
    12. Sinan Q. Salih & Intisar Alakili & Ufuk Beyaztas & Shamsuddin Shahid & Zaher Mundher Yaseen, 2021. "Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 8027-8046, May.
    13. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    14. Ruiming, Fang & Shijie, Song, 2020. "Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network," Agricultural Water Management, Elsevier, vol. 236(C).
    15. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
    16. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    17. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).
    18. 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.
    19. 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).
    20. 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.

    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. 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).
    2. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    4. 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).
    5. 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).
    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. Behrooz Keshtegar & Ozgur Kisi & Hamed Ghohani Arab & Mohammad Zounemat-Kermani, 2018. "Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1101-1116, February.
    8. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    9. Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
    10. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    11. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    12. Songbai Hong & Jinzhi Ding & Fei Kan & Hao Xu & Shaoyuan Chen & Yitong Yao & Shilong Piao, 2023. "Asymmetry of carbon sequestrations by plant and soil after forestation regulated by soil nitrogen," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    14. Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
    15. Zhang, Yixiao & He, Tao & Liang, Shunlin & Zhao, Zhongguo, 2023. "A framework for estimating actual evapotranspiration through spatial heterogeneity-based machine learning approaches," Agricultural Water Management, Elsevier, vol. 289(C).
    16. Song, Lisheng & Bateni, Sayed M. & Xu, Yanhao & Xu, Tongren & He, Xinlei & Ki, Seo Jin & Liu, Shaomin & Ma, Minguo & Yang, Yang, 2021. "Reconstruction of remotely sensed daily evapotranspiration data in cloudy-sky conditions," Agricultural Water Management, Elsevier, vol. 255(C).
    17. Yuanfang Chai & Yao Yue & Louise J. Slater & Jiabo Yin & Alistair G. L. Borthwick & Tiexi Chen & Guojie Wang, 2022. "Constrained CMIP6 projections indicate less warming and a slower increase in water availability across Asia," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    18. Huang, Suo & Bartlett, Paul & Arain, M. Altaf, 2016. "An analysis of global terrestrial carbon, water and energy dynamics using the carbon–nitrogen coupled CLASS-CTEMN+ model," Ecological Modelling, Elsevier, vol. 336(C), pages 36-56.
    19. Tomáš Mikita & Zdeněk Patočka & Elizaveta Avoiani, 2023. "Sap flow modelling based on global radiation and canopy parameters derived from a digital surface model," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 69(8), pages 348-359.
    20. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.

    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:eee:agiwat:v:208:y:2018:i:c:p:140-151. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.