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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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