IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v26y2012i15p4347-4365.html
   My bibliography  Save this article

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

Author

Listed:
  • Hadi Sanikhani
  • Ozgur Kisi
  • Mohammad Nikpour
  • Yagob Dinpashoh

Abstract

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E pan ). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R 2 ), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E pan . The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data. Copyright Springer Science+Business Media Dordrecht 2012

Suggested Citation

  • Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.
  • Handle: RePEc:spr:waterr:v:26:y:2012:i:15:p:4347-4365
    DOI: 10.1007/s11269-012-0148-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-012-0148-4
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11269-012-0148-4?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. Hone-Jay Chu & Liang-Cheng Chang, 2009. "Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 647-660, March.
    2. Abonyi, János & Andersen, Hans & Nagy, Lajos & Szeifert, Ferenc, 1999. "Inverse fuzzy-process-model based direct adaptive control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(1), pages 119-132.
    3. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    4. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    5. Traore, Seydou & Wang, Yu-Min & Kerh, Tienfuan, 2010. "Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone," Agricultural Water Management, Elsevier, vol. 97(5), pages 707-714, May.
    6. Ali-Akbar Sabziparvar & H. Tabari & A. Aeini & M. Ghafouri, 2010. "Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 909-920, March.
    7. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    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. Miao Zhang & Bo Su & Majid Nazeer & Muhammad Bilal & Pengcheng Qi & Ge Han, 2020. "Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China," Land, MDPI, vol. 9(7), pages 1-14, July.
    2. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    3. Seyed Akrami & Ahmed El-Shafie & Othman Jaafar, 2013. "Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3507-3523, July.
    4. Zaher Mundher Yaseen & Majeed Mattar Ramal & Lamine Diop & Othman Jaafar & Vahdettin Demir & Ozgur Kisi, 2018. "Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2227-2245, May.
    5. Ozgur Kisi & Taner Cengiz, 2013. "Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3541-3553, August.
    6. Yeşim Ahi & Çiğdem Coşkun Dilcan & Daniyal Durmuş Köksal & Hüseyin Tevfik Gültaş, 2023. "Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2607-2624, May.

    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. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    2. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    3. Meral Buyukyildiz & Gulay Tezel & Volkan Yilmaz, 2014. "Estimation of the Change in Lake Water Level by Artificial Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4747-4763, October.
    4. Muhammet Emiroglu & Ozgur Kisi, 2013. "Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1473-1488, March.
    5. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    6. Reza Mohammadpour & Aminuddin Ab. Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.
    7. Reza Mohammadpour & Aminuddin Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.
    8. A. Yang & G. Huang & X. Qin, 2010. "An Integrated Simulation-Assessment Approach for Evaluating Health Risks of Groundwater Contamination Under Multiple Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3349-3369, October.
    9. Sabah Saadi Fayaed & Seef Saadi Fiyadh & Wong Jee Khai & Ali Najah Ahmed & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Chow Ming Fai & Suhana Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Ja, 2019. "Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    10. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    11. Maryam Shafaei & Ozgur Kisi, 2016. "Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 79-97, January.
    12. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    13. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    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. Vahid Nourani & Mehdi Komasi & Akira Mano, 2009. "A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 2877-2894, November.
    16. Liu, Zelin & Peng, Changhui & Xiang, Wenhua & Deng, Xiangwen & Tian, DaLun & Zhao, Meifang & Yu, Guirui, 2012. "Simulations of runoff and evapotranspiration in Chinese fir plantation ecosystems using artificial neural networks," Ecological Modelling, Elsevier, vol. 226(C), pages 71-76.
    17. 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.
    18. 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.
    19. Alexandre Evsukoff & Beatriz Lima & Nelson Ebecken, 2011. "Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 963-985, February.
    20. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.

    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:26:y:2012:i:15:p:4347-4365. 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.