Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

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Abstract

The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2003)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

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Deep learning in neural networks: An overview

Jürgen Schmidhuber (2015)

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.