基于IBKA-ACNN-DD的短期土壤温度预测模型

Short-term Soil Temperature Prediction Model Based on IBKA-ACNN-DD

摘要:

土壤温度是农业科学中一个非常重要的变量，其时空变化具有随机性、非线性和非平稳性的特点，极大影响了预测的准确性，为此，提出一种改进黑翅鸢算法（Improved black kite algorithm, IBKA）优化集成注意力机制的卷积神经网络（attention convolutional neural networks, ACNN）和树突网络（dendrite net, DD）的短期土壤温度预测模型。首先，通过自适应分层学习策略改进黑翅鸢算法，以增强算法的优化能力；接着，将集成注意力机制的卷积神经网络和树突网络两种模型融合得到新模型ACNN-DD，用于挖掘土壤温度与特征变量之间的关系，进而输出未来6h内的土壤温度预测结果。最后，为了验证该模型，将重庆市南川区峰岩乡风云村蔬菜种植基地、内蒙古奈曼农田生态系统国家野外科学观测研究站和陕西安塞农田生态系统国家野外科学观测研究站监测的土壤温度数据代入该模型，结果表明，该模型决定系数高达0.98、0.98、0.99，均方根误差低至1.12、1.35、1.37℃，平均绝对百分比误差降到3.83%、5.54%、5.41%，均优于ILSTM_Soil、MLP-FFA和SPA-GA-SVR等传统的土壤温度预测模型。该模型可以有效预测未来6h内的土壤温度，可为智慧农业领域的应用提供理论基础。

Abstract:

Soil temperature is a very important variable in agricultural science, and its spatial and temporal variations are characterized by stochasticity, nonlinearity and non-stationarity, which greatly affects the accuracy of prediction, for this reason, an improved black kite algorithm (IBKA) was proposed to optimize a short-term soil temperature prediction model by integrating the attention mechanism of convolutional neural networks (ACNN) and dendrite networks (DD). Firstly, the black-winged kite algorithm was improved by adaptive hierarchical learning strategy to enhance the optimization ability of the algorithm;then the two models of convolutional neural networks (ACNN) and dendrite networks (DD) with integrated attention mechanism were fused to obtain a new model, ACNN-DD, which was used to mine the relationship between soil temperature and feature variables, and then to output the prediction of the soil temperature in the next 6 hours. Finally, in order to validate the model, soil temperature data monitored at the vegetable planting base in Fengyan Village, Fengyan Township, Nanchuan District, Chongqing, the National Field Scientific Observatory for Naiman Farmland Ecosystems, Inner Mongolia, and the National Field Scientific Observatory for Ansai Farmland Ecosystems, Shaanxi, were brought into the model. The results showed that the coefficients of determination of the model were as high as 0.98, 0.98, and 0.99, and the root mean square errors were as low as 1.12℃, 1.35℃, and 1.37℃, the mean absolute percentage errors were reduced to 3.83%, 5.54%, and 5.41%, which were all superior to that of the traditional soil temperature prediction models, such as ILSTM_Soil, MLP-FFA, and SPA-GA-SVR, etc. This showed that the model can effectively predict the soil temperature in the next 6 hours, which can provide a theoretical basis for the application in the field of smart agriculture.