scholarly journals Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model

2020 ◽  
Vol 12 (21) ◽  
pp. 3654
Author(s):  
Minkyu Kim ◽  
Hyun Yang ◽  
Jonghwa Kim
Keyword(s):  
Prediction Model ◽  
Short Term Memory ◽  
High Water ◽  
Sea Surface ◽  
Series Data ◽  
Economic Losses ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry.

scholarly journals Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 718 ◽  
Author(s):  
Park ◽  
Kim ◽  
Lee ◽  
Kim ◽  
Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Short Term Memory ◽  
Weather Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

scholarly journals Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 684
Author(s):  
Jiateng Song ◽  
Hongbin Wang ◽  
Mingxing Du ◽  
Lei Peng ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Rapid Development ◽  
Series Data ◽  
Load Identification ◽  
Short Term ◽  
Term Memory ◽  
Identification Method ◽  
Proposed Model ◽  
Long Short Term Memory

Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal aggregate error (SAE), the three datasets have increased by 21%, 24%, and 30% respectively. Compared with the existing models, the proposed model has better accuracy and generalization in identifying three open source datasets.

An Unsupervised Learning Model for Pipe Stuck Predictions Using a Long Short-Term Memory Autoencoder Architecture

2021 ◽  
Author(s):  
Yujin Nakagawa ◽  
Tomoya Inoue ◽  
Hakan Bilen ◽  
Konda R. Mopuri ◽  
Keisuke Miyoshi ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Learning Model ◽  
Series Data ◽  
Economic Losses ◽  
Short Term ◽  
Term Memory ◽  
Drilling Operations ◽  
Long Short Term Memory

Abstract Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was an unsupervised learning model built using an encoder-decoder, long short-term memory architecture. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. The trained model was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors increased prior to the pipe sticking in some cases (thereby partly confirming our hypothesis) and were sensitive to large variations in the drilling parameters.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

scholarly journals PREDICTING DAILY PM2.5 USING WEIGHTED LONG SHORT-TERM MEMORY NEURAL NETWORK MODEL

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1451-1455
Author(s):  
H. Fan ◽  
M. Yang ◽  
F. Xiao ◽  
K. Zhao
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Series Data ◽  
Wind Condition ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Pm2.5 Concentration

Abstract. Over the past few decades, air pollution has caused serious damage on public health, thus making accurate predictions of PM2.5 crucial. Due to the transportation of air pollutants among areas, the PM2.5 concentration is strongly spatiotemporal correlated. However, the distribution of air pollution monitoring sites is not even, making the spatiotemporal correlation between the central site and surrounding sites varies with different density of sites, and this was neglected by most existing methods. To tackle this problem, this study proposed a weighted long short-term memory neural network extended model (WLSTME), which addressed the issue that how to consider the effect of the density of sites and wind condition on the spatiotemporal correlation of air pollution concentration. First, several the nearest surrounding sites were chosen as the neighbour sites to the central station, and their distance as well as their air pollution concentration and wind condition were input to multi-layer perception (MLP) to generate weighted historical PM2.5 time series data. Second, historical PM2.5 concentration of the central site and weighted PM2.5 series data of neighbour sites were input into LSTM to address spatiotemporal dependency simultaneously and extract spatiotemporal features. Finally, another MLP was utilized to integrate spatiotemporal features extracted above with the meteorological data of central site to generate the forecasts future PM_2.5 concentration of the central site. Daily PM_2.5 concentration and meteorological data on Beijing–Tianjin–Hebei from 2015 to 2017 were collected to train models and evaluate the performance. Experimental results with 3 other methods showed that the proposed WLSTME model has the lowest RMSE (40.67) and MAE (26.10) and the highest p (0.59). This finding confirms that WLSTME can significantly improve the PM2.5 prediction accuracy.

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