人民珠江2024，Vol.45Issue(6)：73-81,9.DOI:10.3969/j.issn.1001-9235.2024.06.009
基于Attention-BiLSTM混合模型的月尺度降水量预测
Monthly Precipitation Prediction Based on Attention-BiLSTM Model
摘要
Abstract
Precipitation is affected by various meteorological factors,leading to low prediction accuracy.To solve this problem,multiple meteorological factors affecting precipitation were considered,and the attention mechanism was used to assign different weights to various meteorological factors.Combined with the bidirectional long short-term memory neural network(BiLSTM),an improved attention-BiLSTM model was proposed to predict monthly precipitation.By taking the Nanchang Meteorological Station in Jiangxi Province as an example,the observation data of monthly precipitation and meteorological factors(temperature,evaporation,pressure,etc.)from 1989 to 2018 were used as input data for the model.The attention mechanism identified the weights of various meteorological factors to improve the prediction performance of the BiLSTM model for precipitation.The results show that the attention-BiLSTM model can effectively improve the accuracy of precipitation prediction.Through the correction of the attention mechanism,the low precipitation prediction values by the original BiLSTM model are significantly improved.关键词
月尺度降水/气象因子/Attention机制/BiLSTM/预测性能Key words
monthly precipitation/meteorological factors/attention mechanism/BiLSTM/prediction performance分类
建筑与水利引用本文复制引用
成玉祥,肖丽英,王萍根,刘祥周,章晨晖..基于Attention-BiLSTM混合模型的月尺度降水量预测[J].人民珠江,2024,45(6):73-81,9.基金项目
国家自然科学基金项目(52069014、52069015) （52069014、52069015）
2021年江西省学位与研究生教育教学改革研究项目(JXYJG-2021-210) （JXYJG-2021-210）
江西省科技厅项目(20212BDH81002) （20212BDH81002）
南昌工程学院大学生创新创业计划项目(2021011、2022066) （2021011、2022066）
