IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v256y2021ics0378377421003322.html
   My bibliography  Save this article

Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm

Author

Listed:
  • Kisi, Ozgur
  • Khosravinia, Payam
  • Heddam, Salim
  • Karimi, Bakhtiar
  • Karimi, Nazir

Abstract

Determination of wetting patterns’ dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m3 to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS. The outcomes of the assessed method are compared with those of the ANFIS-FCM-PSO, generalized regression neural networks and multivariate adaptive regression splines. In assessing the implemented methods, four commonly used indices, root mean square errors (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe model efficiency (NSE) and graphical methods (e.g., scatter, box plot and Taylor diagrams) are utilized. The benchmark outcomes demonstrate the superiority of new method in estimating wetting front dimensions by improving the accuracy of the ANFIS-FCM-PSO by 29.6%, 18.5%, 6.1%, and 9.0% in estimating the diameter of horizontal redistribution with respect to RMSE, MAE, R2 and NSE, respectively. Furthermore, the ANFIS-FCM-PSOGSA respectively improves the RMSE, MAE, R2 and NSE accuracy of the ANFIS-FCM-PSO by 20.1%, 19.2%, 35.7% and 35.6% in estimating the diameter of downward vertical redistribution. The general outcomes recommend the use of new method in estimating wetting front dimensions of drip irrigation systems.

Suggested Citation

  • Kisi, Ozgur & Khosravinia, Payam & Heddam, Salim & Karimi, Bakhtiar & Karimi, Nazir, 2021. "Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algor," Agricultural Water Management, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003322
    DOI: 10.1016/j.agwat.2021.107067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421003322
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107067?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. Seyedzadeh, Amin & Maroufpoor, Saman & Maroufpoor, Eisa & Shiri, Jalal & Bozorg-Haddad, Omid & Gavazi, Farnoosh, 2020. "Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure," Agricultural Water Management, Elsevier, vol. 228(C).
    2. Kandelous, Maziar M. & Simunek, Jirí, 2010. "Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 97(7), pages 1070-1076, July.
    3. Maroufpoor, Saman & Shiri, Jalal & Maroufpoor, Eisa, 2019. "Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables," Agricultural Water Management, Elsevier, vol. 215(C), pages 63-73.
    4. Al-Ogaidi, Ahmed A.M. & Wayayok, Aimrun & Rowshon, M.K. & Abdullah, Ahmed Fikri, 2016. "Wetting patterns estimation under drip irrigation systems using an enhanced empirical model," Agricultural Water Management, Elsevier, vol. 176(C), pages 203-213.
    5. Kilic, Murat, 2020. "A new analytical method for estimating the 3D volumetric wetting pattern under drip irrigation system," Agricultural Water Management, Elsevier, vol. 228(C).
    6. Elmaloglou, S. & Diamantopoulos, E., 2009. "Effects of hysteresis on redistribution of soil moisture and deep percolation at continuous and pulse drip irrigation," Agricultural Water Management, Elsevier, vol. 96(3), pages 533-538, March.
    7. Singh, D.K. & Rajput, T.B.S. & Singh, D.K. & Sikarwar, H.S. & Sahoo, R.N. & Ahmad, T., 2006. "Simulation of soil wetting pattern with subsurface drip irrigation from line source," Agricultural Water Management, Elsevier, vol. 83(1-2), pages 130-134, May.
    8. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
    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. Yasir L. Alrubaye & Badronnisa Yusuf & Thamer A. Mohammad & Haslinda Nahazanan & Mohamed Azwan Mohamed Zawawi, 2022. "Experimental and Numerical Prediction of Wetting Fronts Size Created by Sub-Surface Bubble Irrigation System," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    2. 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).
    3. Jamei, Mehdi & Maroufpoor, Saman & Aminpour, Younes & Karbasi, Masoud & Malik, Anurag & Karimi, Bakhtiar, 2022. "Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern," Agricultural Water Management, Elsevier, vol. 270(C).
    4. Zong, Rui & Han, Yue & Tan, Mingdong & Zou, Ruihan & Wang, Zhenhua, 2022. "Migration characteristics of soil salinity in saline-sodic cotton field with different reclamation time in non-irrigation season," Agricultural Water Management, Elsevier, vol. 263(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. Jamei, Mehdi & Maroufpoor, Saman & Aminpour, Younes & Karbasi, Masoud & Malik, Anurag & Karimi, Bakhtiar, 2022. "Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern," Agricultural Water Management, Elsevier, vol. 270(C).
    2. Fu, Qiang & Hou, Renjie & Li, Tianxiao & Li, Yue & Liu, Dong & Li, Mo, 2019. "A new infiltration model for simulating soil water movement in canal irrigation under laboratory conditions," Agricultural Water Management, Elsevier, vol. 213(C), pages 433-444.
    3. Qi, Wei & Zhang, Zhanyu & Wang, Ce & Huang, Mingyi, 2021. "Prediction of infiltration behaviors and evaluation of irrigation efficiency in clay loam soil under Moistube® irrigation," Agricultural Water Management, Elsevier, vol. 248(C).
    4. Samad Emamgholizadeh & Amin Seyedzadeh & Hadi Sanikhani & Eisa Maroufpoor & Gholamhosein Karami, 2022. "Numerical and artificial intelligence models for predicting the water advance in border irrigation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 558-575, January.
    5. Ni Gao & Yan Mo & Jiandong Wang & Luhua Yang & Shihong Gong, 2022. "Effects of Flow Path Geometrical Parameters on the Hydraulic Performance of Variable Flow Emitters at the Conventional Water Supply Stage," Agriculture, MDPI, vol. 12(10), pages 1-17, September.
    6. 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).
    7. Vidana Gamage, D.N. & Biswas, A. & Strachan, I.B., 2018. "Actively heated fiber optics method to monitor three-dimensional wetting patterns under drip irrigation," Agricultural Water Management, Elsevier, vol. 210(C), pages 243-251.
    8. Phogat, V. & Šimůnek, J. & Skewes, M.A. & Cox, J.W. & McCarthy, M.G., 2016. "Improving the estimation of evaporation by the FAO-56 dual crop coefficient approach under subsurface drip irrigation," Agricultural Water Management, Elsevier, vol. 178(C), pages 189-200.
    9. Jun Zhang & Lin Li, 2022. "Spatial and Temporal Characteristics of Infiltration Wetting Front of Ring-Shaped Root Emitters," Sustainability, MDPI, vol. 14(11), pages 1-14, May.
    10. Nazari, Ehsan & Besharat, Sina & Zeinalzadeh, Kamran & Mohammadi, Adel, 2021. "Measurement and simulation of the water flow and root uptake in soil under subsurface drip irrigation of apple tree," Agricultural Water Management, Elsevier, vol. 255(C).
    11. Bopp, Carlos & Jara-Rojas, Roberto & Bravo-Ureta, Boris & Engler, Alejandra, 2022. "Irrigation water use, shadow values and productivity: Evidence from stochastic production frontiers in vineyards," Agricultural Water Management, Elsevier, vol. 271(C).
    12. Ali Barzkar & Mohammad Najafzadeh & Farshad Homaei, 2022. "Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model," 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. 110(3), pages 1931-1952, February.
    13. Amin, M.G. Mostofa & Šimůnek, Jirka & Lægdsmand, Mette, 2014. "Simulation of the redistribution and fate of contaminants from soil-injected animal slurry," Agricultural Water Management, Elsevier, vol. 131(C), pages 17-29.
    14. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    15. Kuklik, Vaclav & Hoang, Thai Dai, 2014. "Soil moisture regimes under point irrigation," Agricultural Water Management, Elsevier, vol. 134(C), pages 42-49.
    16. Kilic, Murat, 2020. "A new analytical method for estimating the 3D volumetric wetting pattern under drip irrigation system," Agricultural Water Management, Elsevier, vol. 228(C).
    17. Zhao, Ying & Zhai, Xiafei & Wang, Zhaohui & Li, Huijie & Jiang, Rui & Lee Hill, Robert & Si, Bing & Hao, Feng, 2018. "Simulation of soil water and heat flow in ridge cultivation with plastic film mulching system on the Chinese Loess Plateau," Agricultural Water Management, Elsevier, vol. 202(C), pages 99-112.
    18. Pizarro, E. & Galleguillos, M. & Barría, P. & Callejas, R., 2022. "Irrigation management or climate change ? Which is more important to cope with water shortage in the production of table grape in a Mediterranean context," Agricultural Water Management, Elsevier, vol. 263(C).
    19. Kandelous, Maziar M. & Simunek, Jirí, 2010. "Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 97(7), pages 1070-1076, July.
    20. Karandish, Fatemeh & Šimůnek, Jiří, 2016. "A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies," Agricultural Water Management, Elsevier, vol. 178(C), pages 291-303.

    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:256:y:2021:i:c:s0378377421003322. 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.