scholarly journals TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3797 ◽  
Author(s):  
Jun Liu ◽  
Tong Zhang ◽  
Guangjie Han ◽  
Yu Gou
Keyword(s):  
Short Term Memory ◽  
Global Climate ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Observation Data ◽  
Ocean Temperature ◽  
Temporal Dependence ◽  
Temperature Prediction ◽  
Argo Data ◽  
Matrix Fusion

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.

scholarly journals Prediction of North Atlantic Oscillation Index Associated with the Sea Level Pressure Using DWT-LSTM and DWT-ConvLSTM Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Bin Mu ◽  
Jing Li ◽  
Shijin Yuan ◽  
Xiaodan Luo
Keyword(s):  
North Atlantic Oscillation ◽  
Sea Level ◽  
North Atlantic ◽  
Short Term Memory ◽  
Global Climate ◽  
Time Series Prediction ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Sea Level Pressure ◽  
Level Pressure

The North Atlantic Oscillation (NAO), which manifests as an irregular atmospheric fluctuation, has a profound effect on the global climate change. The NAO index (NAOI) is the quantitative indicator that can reflect the intensity of the NAO events, and its traditional definition is the normalized sea level pressure (SLP) difference between Azores and Iceland. From the variation tendency of the NAOI, we found that it is difficult to predict the NAO with the characteristics of variability and complexity. As a data-driven approach, the deep neural network presents great potential in learning the mechanisms of climate forecasting. In this paper, we adopt long short-term memory (LSTM) and ConvLSTM to predict the NAO from two aspects, NAOI and SLP, respectively. In previous studies, LSTM has been regarded as a resultful method for time series prediction. ConvLSTM can capture both the temporal and spatial interdependencies of the SLP field; then, the NAOI can be calculated from the SLP output. In order to improve the prediction reliability, we utilize the discrete wavelet transform (DWT) as a preprocessing technique to decompose original data into different frequencies, considering the local time dependency. It can effectively preserve the features of high-frequency data and forecast extreme events more accurately. The proposed DWT-LSTM and DWT-ConvLSTM models are compared against multiple advanced models, such as LSTM, Holt-Winters, support vector regression (SVR), and gated recurrent unit (GRU). The results indicate that both DWT-LSTM and DWT-ConvLSTM perform better, particularly at peak values. As for the 31 NAO events from 2006 to 2015, our models achieve the lowest prediction error and the best stability. Compared with the forecast products of CPC named Global Forecast System (GFS) and the ensemble forecasts (ENSM), our models are much closer to observation in multistep forecasting.

scholarly journals Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fan Jiang ◽  
Jitong Ma ◽  
Baosen Wang ◽  
Feifei Shen ◽  
Lingling Yuan
Keyword(s):  
Quality Control ◽  
Rapid Development ◽  
Nonlinear Function ◽  
Observation Data ◽  
Ocean Temperature ◽  
Data Quality Control ◽  
Argo Data ◽  
Data Prediction ◽  
Bidirectional Lstm ◽  
Prediction Approach

With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.

Spatio-temporal dependence-based tensor fusion for thermocline analysis in Argo data

2020 ◽  
pp. 095965182093373
Author(s):  
Yu Jiang ◽  
Shenggeng Lin ◽  
Jinjian Ruan ◽  
Hong Qi
Keyword(s):  
Short Term Memory ◽  
Spatial Dimension ◽  
Future Trend ◽  
Time Dimension ◽  
Temporal Dependence ◽  
Argo Data ◽  
Important Research Topic ◽  
Long Short Term Memory ◽  
Spatio Temporal ◽  
Argo Project

As the ocean data acquired by the Argo project is increasingly huge, how to use artificial intelligence to analyze it so as to discover the distribution and variation of ocean temperature with space and time becomes an important research topic in the world. In this article, a spatio-temporal dependence-based tensor fusion method is proposed, which can be used to determine and analyze the thermocline. In the time dimension, long short-term memory is used to predict the temperature of seawater; in the spatial dimension, the thermocline is found incrementally by using tensor analysis. Experiments on BOA Argo data from 2004 to 2016 show that the proposed method can accurately determine the boundary of the thermocline and predict the future trend of the thermocline.

scholarly journals Public Environment Emotion Prediction Model Using LSTM Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1665 ◽  
Author(s):  
Qiang Zhang ◽  
Tianze Gao ◽  
Xueyan Liu ◽  
Yun Zheng
Keyword(s):  
Optimization Algorithm ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Atmospheric Environment ◽  
Public Attention ◽  
Neural Network Models ◽  
The Public ◽  
Whale Optimization ◽  
Public Environment

Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM’s performance in prediction performance and level decision-making effectiveness and robustness.

scholarly journals Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia

2021 ◽  
Vol 16 (3) ◽  
pp. 497-501
Author(s):  
Sarmad Dashti Latif ◽  
Ali Najah Ahmed
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Global Climate ◽  
Time Series Prediction ◽  
Predictor Variables ◽  
Significant Activity ◽  
Daily Streamflow ◽  
Proposed Model ◽  
Auto Correlation Function ◽  
Conventional Machine

Sustainable management of water supplies faces a comprehensive challenge due to global climate change. Improving forecasts of streamflow based on erratic precipitation is a significant activity nowadays. In recent years, the techniques of data-driven have been widely used in the hydrological parameter’s prediction especially streamflow. In the current research, a deep learning model namely Long Short-Term Memory (LSTM), and two conventional machine learning models namely, Random Forest (RF), and Tree Boost (TB) were used to predict the streamflow of the Kowmung river at Cedar Ford in Australia. Different scenarios proposed to determine the optimal combination of input predictor variables, and the input predictor variables were selected based on the auto-correlation function (ACF). Model output was evaluated using indices of the root mean square error (RMSE), and the Nash and Sutcliffe coefficient (NSE). The findings showed that the LSTM model outperformed RF and TB in predicting the streamflow with RMSE and NSE equal to 102.411, and 0.911 respectively. for the LSTM model. The proposed model could adopt by hydrologists to solve the problems associated with forecasting daily streamflow with high precision. This study may not be generalized because of the geographical condition and the nature of the data for each location.

scholarly journals Seismic Events Prediction Using Deep Temporal Convolution Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yue Geng ◽  
Lingling Su ◽  
Yunhong Jia ◽  
Ce Han
Keyword(s):  
Time Series ◽  
Coal Mine ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Real Life ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Seismic Events ◽  
Prediction Task

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.

scholarly journals The Temperature Prediction of Permanent Magnet Synchronous Machines Based on Proximal Policy Optimization

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 495
Author(s):  
Yuefeng Cen ◽  
Chenguang Zhang ◽  
Gang Cen ◽  
Yulai Zhang ◽  
Cheng Zhao
Keyword(s):  
Permanent Magnet ◽  
Short Term Memory ◽  
Thermal Protection ◽  
Moving Average ◽  
Prediction Method ◽  
Synchronous Machines ◽  
Permanent Magnet Synchronous Machines ◽  
Temperature Prediction ◽  
Permanent Magnet Synchronous Motors ◽  
Policy Optimization

Accurate temperature prediction plays an important role in the thermal protection of permanent magnet synchronous motors. A temperature prediction method of permanent magnet synchronous machines (PMSMs) based on proximal policy optimization is proposed. In the proposed method, the actor-critic framework of reinforcement learning is introduced to model the effective temperature prediction mechanism, and the correlations between the input features are then analyzed to select the appropriate input features. Finally, the simplified proximal policy optimization algorithm is introduced to optimize the value of the prediction temperature of PMSMs. Experimental results reveal the high accuracy and reliability of the proposed method compared with an exponential weighted moving average method (EWMA), a recurrent neural network (RNN), and long short-term memory (LSTM).

scholarly journals 2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2392
Author(s):  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Amedeo Andreotti ◽  
Federico Succetti ◽  
Massimo Panella
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Neural Architecture ◽  
Network Layers ◽  
Learning Scheme ◽  
World Energy ◽  
Multivariate Prediction ◽  
Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

scholarly journals Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

2019 ◽  
Vol 158 ◽  
pp. 6176-6182 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Jiantao Lu ◽  
Liangge Cheng
Keyword(s):  
Solar Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Interval Prediction ◽  
Long Short Term Memory
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