Abstract
As streamflow quantity and drought problem become increasingly severe, it’s imperative than ever to seek next generation machine learning models and learning algorithms which can provide accurate prediction. Reliable prediction of drought variables such as precipitation, soil moisture, and streamflow has been a significant challenge for water resources professionals and water management districts due to their random and nonlinear nature. This paper proposes a long short-term memory networks (LSTM) based deep learning method to predict the historical monthly soil moisture time series data based on the MERRA-Land from 1980 to 2012. The proposed LSTM model learns to predict the value of the next time step at each time step of the time sequence. We also compare the predication accuracy when the network state is updated with the observed values and when the network state is updated with the predicted values. We find that the predictions are more accurate when updating the network state with the observed values instead of the predicted values. In addition, it demonstrated that the proposed method has much lower MSE than the autoregressive integrated moving average model (ARIMA) model and autoregressive model (AR) model.
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Acknowledgment
This work was supported in part by the National Science Foundation (NSF) grants #1505509 and #2011927, DoD grants #W911NF1810475 and #W911NF2010274, NIH grant #1R25AG067896-01, and USGS grant #2020DC142B, and in part by the Foundation of Chongqing Municipal Key Laboratory of Institutions of Higher Education ([2017]3), Foundation of Chongqing Development and Reform Commission (2017[1007]), Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant Nos. KJQN201901218 and KJQN201901203), Natural Science Foundation of Chongqing (Grant No. cstc2019jcyj-bshX0101), Foundation of Chongqing Three Gorges University.
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Zhang, N., Dai, X., Ehsan, M.A., Deksissa, T. (2020). Development of a Drought Prediction System Based on Long Short-Term Memory Networks (LSTM). In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_13
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DOI: https://doi.org/10.1007/978-3-030-64221-1_13
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