地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (8): 1617-1629.doi: 10.12082/dqxxkx.2020.190378
收稿日期:
2019-07-16
修回日期:
2019-10-01
出版日期:
2020-08-25
发布日期:
2020-10-25
通讯作者:
郭金运
E-mail:xinliu1969@126.com;jinyunguo1@126.com
作者简介:
刘 新(1969— ),女,山东肥城人,博士,副教授,主要从事空间数据挖掘和机器学习等研究。E-mail:基金资助:
LIU Xin(), ZHAO Ning, GUO Jinyun*(
), GUO Bin
Received:
2019-07-16
Revised:
2019-10-01
Online:
2020-08-25
Published:
2020-10-25
Contact:
GUO Jinyun
E-mail:xinliu1969@126.com;jinyunguo1@126.com
Supported by:
摘要:
青藏高原的降水量预测不仅为该地区水资源合理规划利用提供依据,同时对中国及周边国家气候变化研究有着重要的意义。论文利用1990—2016年青藏高原降水量数据,采用长短期记忆神经网络(LSTM)对青藏高原月降水量进行预测,主要包括:① 使用青藏高原86个测站1990—2013年的月降水资料,预测各个测站2014—2016年的月降水量,并与传统的RNN、NAR、SSA和ARIMA预测模型相比,平均决定系数R2分别提高了0.07、0.15、0.13和0.36,均方根误差(RMSE)和平均绝对误差(MAE)表现更低;② 分析了降水量预测精度的空间分布特征,将各模型的R2在青藏高原地区内插值,分析R2的空间分布特征,发现所有模型降雨稀少的干旱地区和降雨多的湿润地区R2较低,在气候稳定、降水规律性明显的地区R2较高,且LSTM模型R2≥0.6的空间范围远大于传统模型;③ 分析了不同预测长度对各模型预测精度的影响,发现所有模型会随着预测长度增加而预测精度降低,但在不同的预测长度下LSTM预测的RMSE值都低于其他模型。
刘新, 赵宁, 郭金运, 郭斌. 基于LSTM神经网络的青藏高原月降水量预测[J]. 地球信息科学学报, 2020, 22(8): 1617-1629.DOI:10.12082/dqxxkx.2020.190378
LIU Xin, ZHAO Ning, GUO Jinyun, GUO Bin. Prediction of Monthly Precipitation over the Tibetan Plateau based on LSTM Neural Network[J]. Journal of Geo-information Science, 2020, 22(8): 1617-1629.DOI:10.12082/dqxxkx.2020.190378
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