摘要
使用标准化降水蒸散指数作为农业干旱的监测指标,以站点气象要素和大尺度环流要素为驱动变量,建立干旱预测模型,分析评价传统的整合移动平均自回归(ARIMA)时间序列模型以及不同深度神经网络模型(DNN)的预测效果。结果表明:DNN模型的总体预测能力优于ARIMA模型;同基于长短期记忆网络(LSTM)提出的传统LSTM预测模型(TLSTM)相比,改进的LSTM模型(ILSTM)通过预处理全连接层对预测因子进行非线性映射,能够自动剔除无效信息,提取高层次综合特征,可使预测序列和观测序列的相关系数提升0.04~0.25,均方根误差降低0.07~0.32,误差绝对值的平均降低0.06~0.27;卷积神经网络(CNN)可提取影响干旱变化的大尺度环流信息,其与ILSTM的组合深度网络模型(CLSTM)可进一步使相关系数提升0.03~0.44,均方根误差降低0.09~0.33,误差绝对值的平均降低0.05~0.26。CLSTM模型可应用于短期区域性干旱变化气候预测。
Drought has brought great potential threat to agriculture,ecology,economy,society and available water resources of China,while accurate drought prediction can help risk management and development of early warning system,and it can reduce the destructive impact of drought.Among many prediction methods,data-driven model is a suitable tool with small data demand and fast development speed.With the development of machine learning,especially deep neural network(DNN),deep learning method shows great ability in drought prediction,and is reported to outperform traditional time series model(e.g.integrated moving average autoregressive model,ARIMA).However,its use needs to be widely estimated,further developed and adjusted for geoscience analysis.The standardized precipitation evapotranspiration index(SPEI)is reported to meet the needs of agricultural drought monitoring and early warning under the background of climate warming.SPEI at 1-,3-and 6-month time scale are selected as the quantitative description of agricultural drought,and DNN model driven by meteorological and circulation variable is presented to explore the ability of SPEI prediction at the lead time of 1-3 months.The traditional long short-term memory neural network(TLSTM)has been used in drought prediction,which is limited by the quality of prediction factors and noise.Therefore,an improved TLSTM model(ILSTM)is proposed.With highlight of large-scale climate characteristics,a convolution neural network(CNN)module is combined with the ILSMT model.This newly-developed model(CLSTM)can extract circulation information that contributes to the regional drought change,as well as other outputs of prediction model.Evaluation of the drought prediction capabilities in different models is based on the Pearson correlation coefficient,the root mean square error,and the mean absolute error.Results indicate that overall prediction ability of DNN models outperforms the ARIMA model.And the comparative evaluation results among DNN models show that the architecture of the model has an important impact on the prediction performance.The ILSTM model can extract comprehensive information that contributes to future drought change by nonlinear coding of input variables through the full connected layer.When the correlation coefficient can be raised by 0.04-0.25,the root mean square error can be reduced by 0.07-0.32 and the mean absolute error can be reduced by 0.06-0.27 at the validation stage with different lead time comparing with the TLSTM model.Taking advantage of the circulation information as extra inputs to the ILSTM model,the CLSTM model outperform the ILSTM model,when the correlation coefficient can be raised by 0.03-0.44,the root mean square error can be reduced by 0.09-0.33 and the mean absolute error can be reduced by 0.05-0.26.Both results show that deep learning method has great ability in short-term regional climatic drought prediction.
作者
米前川
高西宁
李玥
李馨仪
唐莹
任传友
Mi Qianchuan;Gao Xining;Li Yue;Li Xinyi;Tang Ying;Ren Chuanyou(College of Agronomy,Shenyang Agricultural University,Shenyang 110866)
出处
《应用气象学报》
CSCD
北大核心
2022年第1期104-114,共11页
Journal of Applied Meteorological Science
基金
国家重点研发计划(2019YFD1002204)。
关键词
干旱预测
标准化降水蒸散指数
长短期记忆网络
卷积神经网络
深度学习
drought prediction
standardized precipitation evapotranspiration index
long and short-term memory network
convolutional neural network
deep learning
