Abstract Using GRACE(gravity recovery and climate experiment) monthly time-variable gravity field data from April 2002 to June 2017, we invert time series of equivalent water height and surface vertical and horizontal deformation in the area of Three Gorges Reservoir, Yangtze River basin and Amazon basin. A deep neural network model based on simple LSTM(long short-term memory) network is applied to predict time series data. The deep LSTM network could be extended deeper by stacking multiple LSTM hidden layers and adding linear layers in output layers. In addition, the attention mechanism is added to increase the ability of long-term characteristics extraction, and the genetic algorithm is used to select the best number of network layers and optimize some hyper parameters. In dynamic predicting mode, the NSE(Nash-Sutcliffe efficiency coefficient) is 0.907 9 at worst and 0.977 7 at best, and the values of R*(scaled root mean square error) are between 0.146 5 and 0.297 5. In static predicting mode, all of NSE values are better than 0.99, and the values of R* are less than 0.062 2, showing that the performance of deep LSTM network is very good.
YAO Zhiwei,CHEN Yu. Time Series Forecasting of Equivalent Water Height and Surface Displacements from GRACE Using Deep Neural Networks[J]. jgg, 2021, 41(7): 721-726.
YAO Zhiwei,CHEN Yu. Time Series Forecasting of Equivalent Water Height and Surface Displacements from GRACE Using Deep Neural Networks[J]. jgg, 2021, 41(7): 721-726.
