基于WOA

花匠小妙招
2024-11-08 05:51

摘要： 准确预测我国林业虫害的发生情况，对提高森林资源风险管控水平以及林业虫害早期预警具有重要意义。研究雄安新区“千年秀林”中美国白蛾的发生和当时的气象环境因素之间的关系，结合群智能优化算法和深度学习算法，提出一种基于WOA-BiLSTM-BA算法的林业虫害预测模型。该算法通过WOA迭代优化BiLSTM的最优参数组合，将注意力机制模块BA引入BiLSTM网络中，以动态分配权重信息，通过全连接层输出预测结果。将提出模型与传统的BP预测模型、LSTM预测模型、BiLSTM预测模型进行对比，结果表明，WOA-BiLSTM-BA模型的效果均优于其他对照预测模型，其决定系数[R²]达到0.989 1，均方根误差[RMSE]仅为0.073，平均百分比误差[MAPE]为0.227 5，平均绝对误差[MAE]为0.056 4。

关键词: 林业害虫, 美国白蛾, 鲸鱼算法, 长短时记忆网络, 注意力机制

Abstract: It is of great significance to accurately predict the occurrence of forest pests in China for improving the level of forest resource risk management and control as well as the early warning of forest pests. The occurrence of forest insect infestation is not only related to temperature and humidity, but also complicated with other meteorological factors. In order to achieve accurate prediction of forest insect infestation, meteorological data and insect infestation data are transformed into a time series prediction problem in this study. In this paper, the relationship between the occurrence of American white moth in the “Millennium Forest” of Xiongan New Area and the meteorological environment at that time was studied, combining swarm intelligent optimization algorithm and deep learning algorithm, a forest pest prediction model based on WOA-BiLSTM-BA algorithm was proposed. Firstly, WOA was used to continuously search for the optimal parameter combination of BiLSTM through iterative optimization to avoid the subjectivity of manual parameter selection and high training cost. Secondly, the Bahdanau Attention module BA was introduced into BiLSTM network to dynamically allocate weight information, and finally the prediction results were output through the fully connected layer. By comparing the proposed model with the traditional BP prediction model, LSTM prediction model and BiLSTM prediction model, the results showed that the effect of WOA-BiLSTM-BA model was better than that of other control prediction models, with R² reaching 0.989 1, RMSE only 0.073, MAPE 0.227 5 and MAE 0.056 4.

Key words: forest pest, American white moth, whale algorithm, long?short?term memory network, attention mechanism

中图分类号:

S763

TP391