IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v270y2022ics0378377422002621.html
   My bibliography  Save this article

Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern

Author

Listed:
  • Jamei, Mehdi
  • Maroufpoor, Saman
  • Aminpour, Younes
  • Karbasi, Masoud
  • Malik, Anurag
  • Karimi, Bakhtiar

Abstract

One of the critical factors in the optimal design of drip-fertigation systems is determining the distribution of nitrate in the soil. Handling such a complex non-linear process is challenging. The main goal of this study is to develop an accurate hybrid Boruta Random Forest (BRF)-Whale Optimization Algorithm (WOA) integrated with an Artificial Neural Network (ANN) to estimate the nitrate concentration (NO3−) in the distribution system. In addition to applying ANN and support vector regression (SVR) methods, various training algorithms and kernel functions are used as standalone validation models to evaluate the robustness of the WOA-ANN model for nitrate pattern estimation. The algorithm uses 11 variables extracted from the experimental study, which are optimally arranged in five input combinations employing the BRF Feature Selection (FS) and regression analyses. The statistical and diagnostic analyses showed that the BRF-FS is the best approach to optimize the WOA-ANN model. The proposed approach provided the best metrics (i.e. R=0.962, RMSE=0.029 mg/L, MAE=0.024, and U95%=0.056) and improved the ANN’s accuracy by 30%. It also outperformed the ANN (R=0.913 and RMSE=0.042 mg/L) and SVR (R=0.901 and RMSE=0.045 mg/L) when applied to estimate the NO3− values. An external validation analysis showed the robustness of all applied machine learning models. Moreover, the significant scoring assessment also showed that when using the BRF-FS approach, the initial nitrate concentration in soil (N0) and nitrate concentration in irrigation water (F−NO3−) had the most influence on the estimation of nitrate pattern, respectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:270:y:2022:i:c:s0378377422002621
    DOI: 10.1016/j.agwat.2022.107715
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422002621
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107715?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. Gardenas, A.I. & Hopmans, J.W. & Hanson, B.R. & Simunek, J., 2005. "Two-dimensional modeling of nitrate leaching for various fertigation scenarios under micro-irrigation," Agricultural Water Management, Elsevier, vol. 74(3), pages 219-242, June.
    2. Hanson, Blaine R. & Simunek, Jirka & Hopmans, Jan W., 2006. "Evaluation of urea-ammonium-nitrate fertigation with drip irrigation using numerical modeling," Agricultural Water Management, Elsevier, vol. 86(1-2), pages 102-113, November.
    3. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    4. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    5. 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).
    6. Al-Ghobari, Hussein M. & El-Marazky, Mohamed S. & Dewidar, Ahmed Z. & Mattar, Mohamed A., 2018. "Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques," Agricultural Water Management, Elsevier, vol. 195(C), pages 211-221.
    7. Anurag Malik & Anil Kumar & Sinan Q Salih & Sungwon Kim & Nam Won Kim & Zaher Mundher Yaseen & Vijay P Singh, 2020. "Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-31, May.
    8. 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.
    9. 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.
    10. Moncef, Hammami & Khemaies, Zayani, 2016. "An analytical approach to predict the moistened bulb volume beneath a surface point source," Agricultural Water Management, Elsevier, vol. 166(C), pages 123-129.
    11. Shuyu Dai & Dongxiao Niu & Yan Li, 2018. "Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm," Energies, MDPI, vol. 11(1), pages 1-25, January.
    12. Jin Li & Maggie Tran & Justy Siwabessy, 2016. "Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-29, February.
    13. Li, Jiusheng & Zhang, Jianjun & Rao, Minjie, 2004. "Wetting patterns and nitrogen distributions as affected by fertigation strategies from a surface point source," Agricultural Water Management, Elsevier, vol. 67(2), pages 89-104, June.
    14. Che, Zheng & Wang, Jun & Li, Jiusheng, 2021. "Effects of water quality, irrigation amount and nitrogen applied on soil salinity and cotton production under mulched drip irrigation in arid Northwest China," Agricultural Water Management, Elsevier, vol. 247(C).
    15. Li, Haoru & Mei, Xurong & Nangia, Vinay & Guo, Rui & Liu, Yuee & Hao, Weiping & Wang, Jiandong, 2021. "Effects of different nitrogen fertilizers on the yield, water- and nitrogen-use efficiencies of drip-fertigated wheat and maize in the North China Plain," Agricultural Water Management, Elsevier, vol. 243(C).
    16. 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).
    17. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(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. Zhou, Hanmi & Ma, Linshuang & Niu, Xiaoli & Xiang, Youzhen & Chen, Jiageng & Su, Yumin & Li, Jichen & Lu, Sibo & Chen, Cheng & Wu, Qi, 2024. "A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain," Agricultural Water Management, Elsevier, vol. 296(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. Azad, Nasrin & Behmanesh, Javad & Rezaverdinejad, Vahid & Abbasi, Fariborz & Navabian, Maryam, 2018. "Developing an optimization model in drip fertigation management to consider environmental issues and supply plant requirements," Agricultural Water Management, Elsevier, vol. 208(C), pages 344-356.
    2. 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).
    3. Arbat, G. & Roselló, A. & Domingo Olivé, F. & Puig-Bargués, J. & González Llinàs, E. & Duran-Ros, M. & Pujol, J. & Ramírez de Cartagena, F., 2013. "Soil water and nitrate distribution under drip irrigated corn receiving pig slurry," Agricultural Water Management, Elsevier, vol. 120(C), pages 11-22.
    4. He, Qinsi & Li, Sien & Kang, Shaozhong & Yang, Hanbo & Qin, Shujing, 2018. "Simulation of water balance in a maize field under film-mulching drip irrigation," Agricultural Water Management, Elsevier, vol. 210(C), pages 252-260.
    5. Barakat, Mohammad & Cheviron, Bruno & Angulo-Jaramillo, Rafael, 2016. "Influence of the irrigation technique and strategies on the nitrogen cycle and budget: A review," Agricultural Water Management, Elsevier, vol. 178(C), pages 225-238.
    6. Wang, Zhen & Li, Jiusheng & Li, Yanfeng, 2014. "Simulation of nitrate leaching under varying drip system uniformities and precipitation patterns during the growing season of maize in the North China Plain," Agricultural Water Management, Elsevier, vol. 142(C), pages 19-28.
    7. Meng, Wenjie & Xing, Jinliang & Niu, Mu & Zuo, Qiang & Wu, Xun & Shi, Jianchu & Sheng, Jiandong & Jiang, Pingan & Chen, Quanjia & Ben-Gal, Alon, 2023. "Optimizing fertigation schemes based on root distribution," Agricultural Water Management, Elsevier, vol. 275(C).
    8. Che, Zheng & Wang, Jun & Li, Jiusheng, 2022. "Modeling strategies to balance salt leaching and nitrogen loss for drip irrigation with saline water in arid regions," Agricultural Water Management, Elsevier, vol. 274(C).
    9. 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).
    10. 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.
    11. Zahra Jafari & Sayed Hamid Matinkhah & Mohammad Reza Mosaddeghi, 2022. "Wetting Patterns in a Subsurface Irrigation System Using Reservoirs of Different Permeabilities: Experimental and HYDRUS-2D/3D Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5335-5352, November.
    12. Li, Yunfeng & Yu, Qihua & Ning, Huifeng & Gao, Yang & Sun, Jingsheng, 2023. "Simulation of soil water, heat, and salt adsorptive transport under film mulched drip irrigation in an arid saline-alkali area using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 290(C).
    13. Phogat, V. & Skewes, M.A. & Cox, J.W. & Alam, J. & Grigson, G. & Šimůnek, J., 2013. "Evaluation of water movement and nitrate dynamics in a lysimeter planted with an orange tree," Agricultural Water Management, Elsevier, vol. 127(C), pages 74-84.
    14. Zhang, You-Liang & Feng, Shao-Yuan & Wang, Feng-Xin & Binley, Andrew, 2018. "Simulation of soil water flow and heat transport in drip irrigated potato field with raised beds and full plastic-film mulch in a semiarid area," Agricultural Water Management, Elsevier, vol. 209(C), pages 178-187.
    15. Mubarak, Ibrahim & Mailhol, Jean Claude & Angulo-Jaramillo, Rafael & Bouarfa, Sami & Ruelle, Pierre, 2009. "Effect of temporal variability in soil hydraulic properties on simulated water transfer under high-frequency drip irrigation," Agricultural Water Management, Elsevier, vol. 96(11), pages 1547-1559, November.
    16. 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.
    17. 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).
    18. Saefuddin, Reskiana & Saito, Hirotaka & Šimůnek, Jiří, 2019. "Experimental and numerical evaluation of a ring-shaped emitter for subsurface irrigation," Agricultural Water Management, Elsevier, vol. 211(C), pages 111-122.
    19. Manuel S. González Canché, 2022. "Post-purchase Federal Financial Aid: How (in)Effective is the IRS’s Student Loan Interest Deduction (SLID) in Reaching Lower-Income Taxpayers and Students?," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(6), pages 933-986, September.
    20. 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.

    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:270:y:2022:i:c:s0378377422002621. 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.