Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

• Main Authors: Zhongwei Liang, Xiaochu Liu, Tao Zou, Jinrui Xiao
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Water
• Subjects: sprinkler irrigation; infiltration effect; intelligent prediction; RSAE–ANFIS; performance evaluation
• Online Access: https://www.mdpi.com/2073-4441/13/6/791

As the high productive efficiency of sprinkler irrigation is largely based on balanced soil moisture distribution, it is essential to study the exact effectiveness of water droplet infiltration, which provides a theoretical basis for rationally scheduling the circulation efficiency of groundwater in agricultural irrigation performance. This research carried out adaptive prediction of the droplet infiltration effectiveness of sprinkler irrigation by using a novel approach of a regularized sparse autoencoder–adaptive network-based fuzzy inference system (RSAE–ANFIS), for the purpose of quantifying actual water droplet infiltration and effectiveness results of precision irrigation in various environmental conditions. The intelligent prediction experiment we implemented could be phased as: the demonstration of governing equations of droplet infiltration for sprinkler irrigation modeling; the measurement and computation of probability densities in water droplet infiltration; innovative establishment and working analysis of RSAE–ANFIS; and the adaptive prediction of infiltration effectiveness indexes, such as average soil moisture depth increment (<i>θ</i>, mm), irrigation infiltration efficiency (<i>e_a</i>, %), irrigation turn duration efficiency (<i>e_t</i>, mm/min), and the uniformity coefficient of soil moisture infiltration (<i>C_u</i>, %), which were implemented to provide a comprehensive illustration for the effective scheduling of sprinkler irrigation. Result comparisons indicated that when jetting pressure (<i>P_w</i>) was 255.2 kPa, the impinge angle (<i>W_a</i>) was 42.5°, the water flow rate (<i>F_a</i>) was 0.67 kg/min, and continuous irrigation time (<i>T_c</i>) was 32.4 min (error tolerance = ±5%, the same as follows), thereby an optimum and stable effectiveness quality of sprinkler irrigation could be achieved, whereas average soil moisture depth increment (<i>θ</i>) was 57.6 mm, irrigation infiltration efficiency (<i>e_a</i>) was 62.5%, irrigation turn duration efficiency (<i>e_t</i>) was 34.5 mm/min, and the uniformity coefficient of soil moisture infiltration (<i>C_u</i>) was 53.6%, accordingly. It could be concluded that the proposed approach of the regularized sparse autoencoder–adaptive network-based fuzzy inference system has outstanding predictive capability and possesses much better working superiority for infiltration effectiveness in accuracy and efficiency; meanwhile, a high agreement between the adaptive predicted and actual measured values of infiltration effectiveness could be obtained. This novel intelligent prediction system has been promoted constructively to improve the quality uniformity of sprinkler irrigation and, consequently, to facilitate the productive management of sprinkler irrigated agriculture.