基于IBAS-BP算法的冬小麦根系土壤含水率预测模型

Prediction Model of Root Soil Moisture Content of Winter Wheat Based on IBAS-BP Algorithm

摘要:

为在节水灌溉系统中精确测量和预测根系土壤含水率，将传统天牛须算法每次迭代过程中的一只天牛改进为一个天牛种群，建立了基于改进天牛须搜索算法优化的IBAS-BP预测模型，并利用实测浅层土壤含水率数据，对深度50cm冬小麦根系土壤含水率进行预测。结果表明，与PSO-BP预测模型、GA-BP预测模型以及原始BAS-BP模型相比，IBAS-BP模型可准确预测冬小麦根系土壤含水率，有效避免了网络陷入局部极小值的可能性，且相对误差均值仅为0.0045。

Abstract:

China’s agricultural water resources are scarce, irrigation water utilization is low, and waste is serious. In water-saving irrigation systems, the precise measurement and prediction of root soil moisture is of great significance. The BAS-Beetle antennae search algorithm performed well in numerical prediction in recent years, but it is greatly affected by the initial value. A beetle in each iteration of the traditional beetle antennae search (BAS) was improved into a beetle population. The IBAS-BP prediction model was established and optimized by the improved beetle antennae search algorithm, and the measured shallow root soil moisture content data was used to predict the soil moisture content of winter wheat root system at a depth of 50cm. The results showed that compared with the PSO-BP prediction model, GA-BP prediction model and the original BAS-BP model, the IBAS-BP model not only effectively avoided the possibility of the network falling into a local minimum, but also had higher prediction accuracy and better robustness. Based on this, accurate predictions can be made on the soil moisture content of the winter wheat root system, which provided a basis for the rational use of agricultural water resources and the construction of water-saving agriculture.