IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i3d10.1007_s11269-017-1857-5.html
   My bibliography  Save this article

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

Author

Listed:
  • Behrooz Keshtegar

    (University of Zabol)

  • Ozgur Kisi

    (Ilia State University)

  • Hamed Ghohani Arab

    (University of Sistan and Baluchestan)

  • Mohammad Zounemat-Kermani

    (Shahid Bahonar University of Kerman)

Abstract

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET0). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET0 was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET0.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:3:d:10.1007_s11269-017-1857-5
    DOI: 10.1007/s11269-017-1857-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-017-1857-5
    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/s11269-017-1857-5?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. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    2. Ali Rahimikhoob, 2016. "Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3063-3075, July.
    3. Ali Rahimikhoob, 2014. "Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 657-669, February.
    4. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    5. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    6. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    7. Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
    8. 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. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    2. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).

    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. 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.
    2. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
    4. 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.
    5. 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.
    6. Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
    7. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
    8. 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.
    9. 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.
    10. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    11. 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).
    12. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    13. Shih-Lun Fang & Yi-Shan Lin & Sheng-Chih Chang & Yi-Lung Chang & Bing-Yun Tsai & Bo-Jein Kuo, 2024. "Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables," Agriculture, MDPI, vol. 14(4), pages 1-20, March.
    14. 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.
    15. 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).
    16. M. Majidi & A. Alizadeh & M. Vazifedoust & A. Farid & T. Ahmadi, 2015. "Analysis of the Effect of Missing Weather Data on Estimating Daily Reference Evapotranspiration Under Different Climatic Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2107-2124, May.
    17. 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.
    18. Junzeng Xu & Junmei Wang & Qi Wei & Yanhua Wang, 2016. "Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2055-2073, April.
    19. 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).
    20. Xiaodong Ren & Zhongyi Qu & Diogo S. Martins & Paula Paredes & Luis S. Pereira, 2016. "Daily Reference Evapotranspiration for Hyper-Arid to Moist Sub-Humid Climates in Inner Mongolia, China: I. Assessing Temperature Methods and Spatial Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3769-3791, September.

    More about this item

    Keywords

    Reference evapotranspiration; ANFIS; M5 model tree; ANN; Subset ANFIS;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

    Statistics

    Access and download statistics

    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:32:y:2018:i:3:d:10.1007_s11269-017-1857-5. 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.