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Sprinkler Drip Infiltration Quality Prediction for Moisture Space Distribution Using RSAE-NPSO

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  • Zhongwei Liang

    (Guangdong Engineering Research Centre for Highly Efficient Utility of Water/Fertilizers and Solar-Energy Intelligent Irrigation, Guangzhou University, Guangzhou 510006, China
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    Advanced Institute of Engineering Science for Intelligent Manufacturing, Guangzhou University, Guangzhou 510006, China)

  • Tao Zou

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    Advanced Institute of Engineering Science for Intelligent Manufacturing, Guangzhou University, Guangzhou 510006, China)

  • Yupeng Zhang

    (China-Ukraine Institute of Welding, Guangdong Academy of Sciences, Guangzhou 510650, China)

  • Jinrui Xiao

    (Guangdong Engineering Research Centre for Highly Efficient Utility of Water/Fertilizers and Solar-Energy Intelligent Irrigation, Guangzhou University, Guangzhou 510006, China
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Xiaochu Liu

    (Guangdong Engineering Research Centre for Highly Efficient Utility of Water/Fertilizers and Solar-Energy Intelligent Irrigation, Guangzhou University, Guangzhou 510006, China
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    Advanced Institute of Engineering Science for Intelligent Manufacturing, Guangzhou University, Guangzhou 510006, China)

Abstract

Considering the high quality requirements related to agricultural production, the intelligent prediction of sprinkler drip infiltration quality (SDIQ) of the moisture space distribution in soil fields is an important issue in precision irrigation. The objective of this research is to adaptively predict an optimal data set of SDIQ indices using a robust prediction algorithm called the regulated sparse autoencoder–niche particle swarm optimization (RSAE-NPSO) system, so that the SDIQ indices of various irrigated layers of loam, sandy, chernozem, saline–alkali, and clay soils can be predicted and analyzed. This prediction procedure involves the following steps. First, the drip infiltration effectiveness of the moisture on specific irrigated soil layers is measured. Second, a complete set of SDIQ indices used for assessing the moisture space distribution is introduced. Third, an analytical framework based on the RSAE-NPSO algorithm is established. Fourth, the intelligent prediction of SDIQ indices using RSAE-NPSO computation is achieved. This research indicates that when the irrigation parameters include the sprinkling pressure ( P w ) at 224.8 KPa, irrigation duration time ( I d ) at 2.68 h, flow discharge amount ( F q ) at 1682.5 L/h, solar radiation ( S r ) at 17.2 MJ/m 2 , average wind speed ( A w ) at 1.18 m/s, average air temperature ( A t ) at 22.8 °C, and average air relative humidity ( A h ) at 72.8%, as well as the key variables of the irrigation environment, including the soil bulk density ( S b ) at 1.68 g/cm 3 , soil porosity ( S p ) at 68.7%, organic carbon ratio ( O c ) at 63.5%, solute transportation coefficient ( S t ) at 4.86 × 10 −6 , evapotranspiration rate ( E v ) at 33.8 mm/h, soil saturated hydraulic conductivity rate ( S s ) at 4.82 cm/s, soil salinity concentration ( S c ) at 0.46%, saturated water content ( S w ) at 0.36%, and wind direction W d in the north–northwest direction (error tolerance = ±5%, the same as follows), an optimal data set of SDIQ indices can be ensured, as shown by the exponential entropy of the soil infiltration pressure (ESIP) at 566.58, probability of moisture diffusivity (PMD) at 96.258, probabilistic density of infiltration effectiveness (PDIE) at 98.224, modulus of surface radial runoff (MSRR) at 411.25, infiltration gradient vector (IGV) at [422.5,654.12], and normalized infiltration probabilistic coefficient (NIPC) at 95.442. The quality inspection of the SDIQ prediction process shows that a high agreement between the predicted and actual measured SDIQ indices is achieved. RSAE-NPSO has extraordinary predictive capability and enables much better performance than the other prediction methods in terms of accuracy, stability, and efficiency. This novel prediction method can be used to ensure the infiltration uniformity of the moisture space distribution in sprinkler drip irrigation. It facilitates productive SDIQ management for precision soil irrigation and agricultural crop production.

Suggested Citation

  • Zhongwei Liang & Tao Zou & Yupeng Zhang & Jinrui Xiao & Xiaochu Liu, 2022. "Sprinkler Drip Infiltration Quality Prediction for Moisture Space Distribution Using RSAE-NPSO," Agriculture, MDPI, vol. 12(5), pages 1-32, May.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:691-:d:814980
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    as
    1. Lena, Bruno Patias & Bondesan, Luca & Pinheiro, Everton Alves Rodrigues & Ortiz, Brenda V. & Morata, Guilherme Trimer & Kumar, Hemendra, 2022. "Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA," Agricultural Water Management, Elsevier, vol. 259(C).
    2. Yu, Qihua & Kang, Shaozhong & Hu, Shunjun & Zhang, Lu & Zhang, Xiaotao, 2021. "Modeling soil water-salt dynamics and crop response under severely saline condition using WAVES: Searching for a target irrigation volume for saline water irrigation," Agricultural Water Management, Elsevier, vol. 256(C).
    3. 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.
    4. Zhongwei Liang & Shaopeng Liao & Yiheng Wen & Xiaochu Liu, 2019. "Working parameter optimization of strengthen waterjet grinding with the orthogonal-experiment-design-based ANFIS," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 833-854, February.
    5. Mondaca-Duarte, F.D. & van Mourik, S. & Balendonck, J. & Voogt, W. & Heinen, M. & van Henten, E.J., 2020. "Irrigation, crop stress and drainage reduction under uncertainty: A scenario study," Agricultural Water Management, Elsevier, vol. 230(C).
    6. X. C. Cao & R. Shu & X. P. Guo & W. G. Wang, 2019. "Scarce water resources and priority irrigation schemes from agronomic crops," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(3), pages 399-417, March.
    7. González Perea, R. & Camacho Poyato, E. & Montesinos, P. & Rodríguez Díaz, J.A., 2018. "Prediction of applied irrigation depths at farm level using artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 206(C), pages 229-240.
    8. Jie, Feilong & Fei, Liangjun & Li, Shan & Hao, Kun & Liu, Lihua & Zhu, Hongyan, 2021. "Prediction model for irrigation return flow considering lag effect for arid areas," Agricultural Water Management, Elsevier, vol. 256(C).
    9. Nie, Wei-Bo & Li, Yi-Bo & Zhang, Fan & Ma, Xiao-Yi, 2019. "Optimal discharge for closed-end border irrigation under soil infiltration variability," Agricultural Water Management, Elsevier, vol. 221(C), pages 58-65.
    10. Wang, JiaJia & Long, HuaiYu & Huang, YuanFang & Wang, XiangLing & Cai, Bin & Liu, Wei, 2019. "Effects of different irrigation management parameters on cumulative water supply under negative pressure irrigation," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    11. Mattar, M.A. & Alazba, A.A. & Zin El-Abedin, T.K., 2015. "Forecasting furrow irrigation infiltration using artificial neural networks," Agricultural Water Management, Elsevier, vol. 148(C), pages 63-71.
    12. 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).
    13. Yahyaoui, Imene & Tadeo, Fernando & Segatto, Marcello Vieira, 2017. "Energy and water management for drip-irrigation of tomatoes in a semi- arid district," Agricultural Water Management, Elsevier, vol. 183(C), pages 4-15.
    14. Sun, Miao & Gao, Xuerui & Zhang, Yulin & Song, Xiaolin & Zhao, Xining, 2022. "A new solution of high-efficiency rainwater irrigation mode for water management in apple plantation: Design and application," Agricultural Water Management, Elsevier, vol. 259(C).
    15. AL-Kayssi, A.W. & Mustafa, S.H., 2016. "Modeling gypsifereous soil infiltration rate under different sprinkler application rates and successive irrigation events," Agricultural Water Management, Elsevier, vol. 163(C), pages 66-74.
    16. Hamilton, G.J. & Akbar, G. & Raine, S. & McHugh, A., 2020. "Deep blade loosening and two-dimensional infiltration theory make furrow irrigation predictable, simpler and more efficient," Agricultural Water Management, Elsevier, vol. 239(C).
    17. Thorp, K.R. & Thompson, A.L. & Bronson, K.F., 2020. "Irrigation rate and timing effects on Arizona cotton yield, water productivity, and fiber quality," Agricultural Water Management, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

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