Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (8): 1617-1629.doi: 10.12082/dqxxkx.2020.190378

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Prediction of Monthly Precipitation over the Tibetan Plateau based on LSTM Neural Network

LIU Xin(), ZHAO Ning, GUO Jinyun*(), GUO Bin   

  1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  • 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:
    National Natural Science Foundation of China(41774001);The Basic Science and Technology Project of China(2015FY310200)

Abstract:

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.

Key words: LSTM neural network, precipitation, prediction, RNN neural network, Qinghai-Tibet Plateau, time series, machine learning