Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks

Wei, Xin; Zhang, Lulu; Yang, Haoqing; Zhang, Li Min; Yao, Yanbin

doi:10.1016/j.gsf.2020.04.011

Cited by 141 publications

(44 citation statements)

References 50 publications

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“…An RNN is an artificial neural network wherein adjacent hidden neurons are connected [39]. These recurrent structures of RNNs can transfer time dependence through hidden units and consider temporal correlations.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

Niu

Wang

et al. 2021

Applied Sciences

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As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Theoretically, RNN can take advantage of all information no matter how long the sequences are. However, according to previous studies, because of the vanishing gradient problem, standard RNNs are suitable only for short-term dependencies [39,40].…”

Section: Standard Rnnmentioning

confidence: 99%

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

Niu

Wang

et al. 2021

Applied Sciences

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show abstract

“…The study proposes Transductive LSTM (T-LSTM) which exploits the local information in time-series prediction. The [39] proposes a long period timeline series data prediction function, which supports predict future data variation trends by month as well as high prediction accuracy. In [40], a typical neural network-based tine line data prediction method is proposed, which is applicable for different application scenarios.…”

Section: Related Workmentioning

confidence: 99%

Performance Prediction Method for Stream Computing Platform Based on Time Series

Lü

2021

IEEE Access

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As one of the most popular high-performance data processing technology, existing task and resource scheduling strategies for stream computing platforms are suffering from the problem of triggering hysteresis, which seriously affects the cluster performance. To address this problem, the idea of performance prediction based on timeline series data is proposed. Firstly, the performance variation rule of the stream processing platform is analyzed, which provides a basis for proposing the performance prediction model. Secondly, the basic topology paradigm, periodic load prediction model, and real-time throughput prediction model are proposed as the theoretical foundation for performance prediction. Thirdly, the performance prediction algorithm is proposed to predict the variation trend of the processing load and throughput of the cluster. The processing load is predicted periodically while the throughput of the cluster is predicted in a real-time manner. Finally, the performance evaluation algorithm is proposed to evaluate the cluster performance based on the prediction results and trigger the corresponding scheduling strategies in advance. The experimental results showed that the prediction accuracy of the proposed algorithm meets the requirements in practical applications. Meanwhile, the proposed method improves the performance of stream computing performance by triggering the scheduling strategies in advance.INDEX TERMS Big data, stream computing, performance prediction, processing load, and throughput.

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“…The traditional RNN in solving the association between long sequences, through practice, proved that the classical RNN performs poorly, the reason is that when backpropagation is performed, too long sequences lead to abnormal computation of gradients, and gradient disappearance or explosion occurs. LSTM [6] long short-term memory model is a special kind of RNN neural network and effectively deal with long-term dependence problem, it is compared with RNN in two aspects to do improvement The LSTM gating mechanism has three main gates which are forgetting gate, input gate and output gate respectively. The equation of LSTM network structure at moment t is shown below, f t , i t , o t , C t are forgetting gate, input gate, output gate and cell state respectively, W t is each gating weight parameter, b t is each gating bias parameter respectively,  are sigmoid activation function, and tanh is the hyperbolic tangent activation function.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning in Product Manufacturing Record System

Wang

Zhao

2021

International Journal of Advanced Network, Monitoring and Controls

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Deep learning based data analysis techniques are investigated in the context of product production record systems, using CNN, STACK LSTM, GRU, INCEPTION, ConvLSTM and CasualLSTM techniques to design network models and to study the processing of temporal data. Three network models are proposed for the problem of predicting the pass rate of upcoming product inspection records, namely CNN-STACK LSTM, INCEPTION-GRU and INCEPTION-Casual LSTM, and the structure of each network model follows the learning of local-global features. The experimental results show that the INCEPTION-GRU network model works best among the three models. Based on the prediction results, it is possible to correct in advance the operation of the shop technicians who do not regulate the debugging of the product, so that the initial production efficiency of the product can be improved.

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Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks

Cited by 141 publications

References 50 publications

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

Performance Prediction Method for Stream Computing Platform Based on Time Series

Deep Learning in Product Manufacturing Record System

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