IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i6d10.1007_s11269-021-02810-w.html
   My bibliography  Save this article

Evaluating the Performance of Agricultural Water Distribution Systems Using FIS, ANN and ANFIS Intelligent Models

Author

Listed:
  • Habibeh Sharifi

    (University of Tehran)

  • Abbas Roozbahani

    (University of Tehran)

  • Seied Mehdy Hashemy Shahdany

    (University of Tehran)

Abstract

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02810-w
    DOI: 10.1007/s11269-021-02810-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02810-w
    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/s11269-021-02810-w?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. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    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. Hassani, Yousef & Hashemy Shahdany, Seied Mehdy & Maestre, J.M. & Zahraie, Banafsheh & Ghorbani, Mohammad & Henneberry, Shida Rastegari & Kulshreshtha, Suren N., 2019. "An economic-operational framework for optimum agricultural water distribution in irrigation districts without water marketing," Agricultural Water Management, Elsevier, vol. 221(C), pages 348-361.
    4. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    5. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    6. Morteza Babaei & Abbas Roozbahani & S. Mehdy Hashemy Shahdany, 2018. "Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 4079-4101, September.
    7. Kaghazchi, Afsaneh & Hashemy Shahdany, S. Mehdy & Roozbahani, Abbas, 2021. "Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model," Agricultural Water Management, Elsevier, vol. 245(C).
    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. Costabile, Pierfranco & Costanzo, Carmelina & Gangi, Fabiola & De Gaetani, Carlo Iapige & Rossi, Lorenzo & Gandolfi, Claudio & Masseroni, Daniele, 2023. "High-resolution 2D modelling for simulating and improving the management of border irrigation," Agricultural Water Management, Elsevier, vol. 275(C).
    2. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.
    3. Laís Régis Salvino & Heber Pimentel Gomes & Saulo de Tarso Marques Bezerra, 2022. "Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2779-2793, June.

    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. Afsaneh Kaghazchi & Seied Mehdy Hashemy Shahdany & Alireza Firoozfar, 2022. "Prioritization of agricultural water distribution operating systems based on the sustainable development indicators," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 23-40, February.
    2. Barkhordari, Soroush & Hashemy Shahdany, Seied Mehdy, 2021. "Developing a smart operating system for fairly distribution of irrigation water, based on social, economic, and environmental considerations," Agricultural Water Management, Elsevier, vol. 250(C).
    3. Atiyeh Bozorgi & Abbas Roozbahani & Seied Mehdy Hashemy Shahdany & Rouzbeh Abbassi, 2021. "Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3139-3159, August.
    4. Karimi Avargani, Habib & Hashemy Shahdany, S. Mehdy & Hashemi Garmdareh, S. Ebrahim & Liaghat, Abdolmajid & Guan, Guanghua & Behzadi, Farhad & Milan, Sami Ghordoyee & Berndtsson, Ronny, 2023. "Operational loss estimation in irrigation canals by integrating hydraulic simulation and crop growth modeling," Agricultural Water Management, Elsevier, vol. 288(C).
    5. Kostić, Srđan & Stojković, Milan & Guranov, Iva & Vasović, Nebojša, 2019. "Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 408-421.
    6. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    7. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    8. Ao, Chang & Zeng, Wenzhi & Wu, Lifeng & Qian, Long & Srivastava, Amit Kumar & Gaiser, Thomas, 2021. "Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Jolfan, Mohsen Hosseini & Hashemy Shahdany, S. Mehdy & Javadi, Saman & Milan, Sami Ghordoyee & Neshat, Aminreza & Berndtsson, Ronny & Tork, Hamed, 2023. "Modernization in agricultural water distribution system for aquifer storage and recovery – A case study," Agricultural Water Management, Elsevier, vol. 282(C).
    10. Dilip Kumar Roy & Bithin Datta, 2017. "Fuzzy C-Mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 355-376, January.
    11. Bahrudin Hrnjica & Ognjen Bonacci, 2019. "Lake Level Prediction using Feed Forward and Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2471-2484, May.
    12. Sangita Dey & Arabin Kumar Dey & Rajesh Kumar Mall, 2021. "Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3395-3410, August.
    13. Indrajit Mandal & Swades Pal, 2022. "Assessing the impact of ecological insecurity on ecosystem service value in stone quarrying and crushing dominated areas," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 11760-11784, October.
    14. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
    15. 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).
    16. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    17. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    18. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    19. Javad Shafiee Neyestanak & Abbas Roozbahani, 2021. "Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 5049-5072, November.
    20. Avargani, Habib Karimi & Hashemy Shahdany, S. Mehdy & Kamrani, Kazem & Maestre, Jose, M. & Hashemi Garmdareh, S. Ebrahim & Liaghat, Abdolmajid, 2022. "Prioritization of surface water distribution in irrigation districts to mitigate crop yield reduction during water scarcity," Agricultural Water Management, Elsevier, vol. 269(C).

    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:35:y:2021:i:6:d:10.1007_s11269-021-02810-w. 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.