计算机与数字工程2024，Vol.52Issue(6)：1598-1603,6.DOI:10.3969/j.issn.1672-9722.2024.06.002
基于CFSFDP-RBF神经网络的加拿大区域气候预测
Regional Climate Prediction for Canada Based on CFSFDP-RBF Neural Network
摘要
Abstract
The melting of Antarctic glaciers,increasing hurricanes and rising sea levels have made people realize that global warming is posing a great challenge to human survival.To address the problem of variable temperature change caused by global warming and to accurately predict the temperature change,this paper takes some regions of Canada as an example and proposes an improved radial basis function(RBF)neural network climate prediction model by preprocessing the climate data of 10 Canadian provinces and finally screening out the data of 4 provinces with more complete data retention.The model uses the Clustering by Fast Search and Find of Density Peaks(CFSFDP)algorithm and Adaptive Moment Estimation(Adam)to optimize the RBF neural net-work.The CFSFDP algorithm is first used to cluster out the central clusters to determine the RBF neural network path base centers to avoid the errors caused by random selection.Then the Adam algorithm is used to iteratively differentiate the objective function and adjust the weights,while adaptively changing the learning rate to improve the prediction accuracy.The accuracy of the model is test-ed by comparing it with BP neural network,RBF neural network,K-means optimized RBF neural network and the algorithm of this paper,and the accuracy of the model is found to be quite high.To test the accuracy of the results,the improved integrated Autore-gressive Integrated Moving Average model(ARIMA),Vector Autoregressive Model(VAR)and CFSFDP-RBF neural network algo-rithm were used to predict the climate,and the results of the three models were similar,indicating that the prediction results of this algorithm are reliable.The experimental results show that the average temperature and precipitation will reach 15.047 0℃and 2.098 4 mm respectively in the next 25 years,with a prediction accuracy of more than 95%.关键词
时序数据/密度峰值快速聚类/自适应矩估计/径向基神经网络/气候预测Key words
time series data/clustering by fast search and find of density peaks/adaptive moment estimation/radial basis function neural network/climate prediction分类
信息技术与安全科学引用本文复制引用
寇露彦,李学俊,廖竞,熊建华,吴昌述..基于CFSFDP-RBF神经网络的加拿大区域气候预测[J].计算机与数字工程,2024,52(6):1598-1603,6.基金项目
国防基础计划科研项目(编号:JCKY2019204B007) （编号:JCKY2019204B007）
国家自然科学基金面上项目(编号:61872304)资助. （编号:61872304）
