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

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  • 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
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    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).
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    Cited by:

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    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.

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