Journal of Geo-information Science >
Prediction of Monthly Precipitation over the Tibetan Plateau based on LSTM Neural Network
Received date: 2019-07-16
Request revised date: 2019-10-01
Online published: 2020-10-25
Supported by
National Natural Science Foundation of China(41774001)
The Basic Science and Technology Project of China(2015FY310200)
Copyright
Precipitation prediction on the Qinghai-Tibet Plateau not only provides a basis for rational planning and utilization of water resources, but also has significance for climate change research in China and neighboring countries. In this paper, the Long Short Term Memory neural network (LSTM) was used to predict the monthly precipitation over the Qinghai-Tibet Plateau using data from 1990 to 2016. Firstly, the monthly precipitation data of 86 stations in the Qinghai-Tibet Plateau from 1990 to 2013 were used to predict the monthly precipitation of each station from 2014 to 2016. Comparing with the traditional RNN, NAR, SSA, and ARIMA prediction models, LSTM increased the average coefficient of determination (R2) by 0.07, 0.15, 0.13, and 0.36, respectively. Simultaneously, LSTM had lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Among them, the observation of station 56106 showed that the LSTM model predicted the period more accurately with less displacement deviation, and that the prediction of the valley between July and September was more accurate with R2 reaching 0.87. Secondly, the spatial distribution characteristics of precipitation prediction accuracy were analyzed. The R2of each model was interpolated in the Qinghai-Tibet Plateau, and the spatial distribution characteristics of R2were analyzed. All the drought areas with rare rainfall and the wet areas with heavy rainfall were of lower R2, while the areas with stable climate and obvious precipitation were of higher R2. Areas of R2 over 0.6 were much larger when using the LSTM model than the traditional model. Finally, influence of different prediction lengths on the prediction accuracy was analyzed for each model. All models showed decreased prediction accuracy as the prediction length increased, yet the RMSE values predicted by LSTM were lower than by other models with the varying prediction lengths.
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
表1 ARMA模型类型及阶数确定Tab. 1 ARMA model type and order determination |
自相关系数 | 偏相关系数 | 模型定阶 |
---|---|---|
拖尾 | p阶截尾 | AR(p)模型 |
q阶截尾 | 拖尾 | MA(q)模型 |
拖尾 | 拖尾 | ARMA(p, q)模型 |
表2 56106号测站不同模型精度评价指标对比Tab. 2 Comparison of different model accuracy evaluation indexes at station 56106 |
评价指标 | LSTM | RNN | NAR | SSA | ARIMA |
---|---|---|---|---|---|
RMSE | 19.69 | 29.82 | 32.15 | 33.77 | 54.46 |
MAE | 14.06 | 18.57 | 24.17 | 22.17 | 40.67 |
R2 | 0.87 | 0.70 | 0.65 | 0.61 | 0.28 |
图10 青藏高原所有测站不同模型的降水量预测精度对比Fig. 10 Comparison of precipitation prediction accuracy of the different models at 86 stations on the Qinghai-Tibet Plateau |
表3 5种模型在青藏高原降水量预测精度对比Tab. 3 Comparison of prediction accuracy of the five models at 86 stations |
评价指标 | LSTM | RNN | NAR | SSA | ARIMA |
---|---|---|---|---|---|
RMSE | 26.83 | 28.87 | 31.64 | 30.17 | 38.01 |
MAE | 17.91 | 20.65 | 22.77 | 21.04 | 26.47 |
R2 | 0.61 | 0.55 | 0.46 | 0.48 | 0.25 |
图12 不同模型在预测长度分别为6、12、24和36个月时RMSE的变化Fig. 12 RMSE changes of the different models at predicted lengths of 6, 12, 24, and 36 months respectively |
表4 不同模型在不同预测长度下的平均RMSETab. 4 Average RMSEs of the different models at different predicted lengths |
预测长度/个月 | LSTM | RNN | NAR | SSA | ARIMA |
---|---|---|---|---|---|
36 | 26.83 | 28.87 | 31.64 | 30.17 | 38.01 |
24 | 26.23 | 28.16 | 30.45 | 29.37 | 35.82 |
12 | 25.21 | 27.52 | 29.95 | 28.78 | 36.24 |
6 | 19.89 | 23.85 | 26.58 | 25.10 | 27.30 |
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