不同深度基质含水率变化规律与预测模型研究

Variation Law and Prediction Model of Substrate Moisture Content at Different Depths

为探明不同深度的基质含水率变化规律,使用干燥法分别对多个EC5型传感器进行校准,并将4个传感器分别放置垂向距滴头5、10、15、20 cm 4个不同深度处,测量不同滴头流量及滴灌量条件下垂向基质含水率的变化,建立了不同深度基质含水率预测模型。试验结果表明,在滴灌开始后第1层(距滴头5 cm处)基质含水率最先上升并迅速达到较高水平,滴灌停止后水分将快速扩散至更深基质层,其含水率可提升至根系易利用水平(25.3%及以上),水分快速运移时间持续1 h左右,随着初始基质含水率的降低,在相同滴头流量及灌溉量条件下,水分在垂直方向的运移程度更深,将第1层基质初始含水率、滴灌时间、预测时间、预测层高度差、滴头流量作为输入,利用遗传算法优化的BP神经网络算法与随机森林回归算法(RFR),建立滴灌下基质不同深度含水率预测模型。将试验所预测的滴灌后基质含水率与实际测量的不同深度基质含水率进行对比分析,并对不同预测深度的预测结果进行误差分析,结果表明GABP预测模型及RFR预测模型的R2分别为0.8664、0.9465,即RFR算法建立的预测模型更加精确,并且预测深度越接近于第1层基质预测结果越准确。

In order to ascertain the change law of the substrate moisture content at different depths,drying method was used to calibrate multiple EC5 sensors,and the four sensors were placed at four different depths,i.e.,5 cm,10 cm,15 cm and 20 cm vertically from the dripper.The changes of the vertical substrate water content under different dripper flow and drip irrigation conditions were measured,and a prediction model of substrate water content at different depths was established.The test results showed that the substrate moisture content of the first layer(5 cm away from the dripper)was risen first after the drip irrigation started and quickly reached a higher level.After the drip irrigation stopped,the moisture would quickly diffuse to the deeper substrate layer,and its moisture content can be increased to the root system easy to use level(25.3%and above),the rapid water migration time lasted for about 1 h.With the decrease of the initial substrate moisture content,under the same dripper flow and irrigation conditions,the degree of water migration in the vertical direction was deeper.The initial water content of the first layer of the substrate,drip irrigation time,prediction time,predicted layer height difference,and dripper flow were used as input,and genetic algorithm optimized BP neural network algorithm and random forest regression algorithm(RFR)were used to establish different depths of water content of the substrate under drip irrigation rate prediction model.The predicted water content of the substrate after drip irrigation in the experiment compared with the actual measured water content of the substrate at different depths,and the error analysis was performed on the prediction results of different prediction depths.The results showed that the prediction accuracy(R2)of the GABP prediction model and the RFR prediction model were 0.8664 and 0.9465,respectively,that was,the prediction model established by the RFR algorithm was more accurate,and the closer the prediction depth was to the first layer,the more accurate the prediction result was.