LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion

Lyu, Pingyang; Chen, Ning; Mao, Shanjun; Mei, Li

doi:10.1016/j.psep.2020.02.021

Cited by 71 publications

(23 citation statements)

References 15 publications

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“…Performance is compared between Persistence Model, Historical Average, AutoRegressive Integrated Moving Average (ARIMA), Feedforward Neural Network (FNN), LSTM (GeoTab), and LSTM (GeoTab+WU). We selected the baseline models that are widely accepted by literature such as Hyndman and Athanasopoulos [34], Du et al [35], and Lyu et al [36], and they each represent a different type of time series forecasting model. All models are tested on the same GeoTab grids with a missing data ratio of up to 5.5% to predict the 24-hour surface temperature for the predetermined 27 testing days, given 48-hour previous observations.…”

Section: A Overall Performance Of the Predictors In Comparisonmentioning

confidence: 99%

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Yu¹,

et al. 2021

IEEE Access

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The rising temperature is one of the key indicators of a warming climate, capable of causing extensive stress to biological systems as well as built structures.Ambient temperature collected at ground level can have higher variability than regional weather forecasts, which fail to capture local dynamics. There remains a clear need for accurate air temperature prediction at the suburban scale at high temporal and spatial resolutions. This research proposed a framework based on a long short-term memory (LSTM) deep learning network to generate day-ahead hourly temperature forecasts with high spatial resolution. Air temperature observations are collected at a very fine scale (∼150m) along major roads of New York City (NYC) through the Internet of Things (IoT) data for 2019-2020. The network is a stacked two layer LSTM network, which is able to process the measurements from all sensor locations at the same time and is able to produce predictions for multiple future time steps simultaneously. Experiments showed that the LSTM network outperformed other traditional time series forecasting techniques, such as the persistence model, historical average, AutoRegressive Integrated Moving Average (ARIMA), and feedforward neural networks (FNN). In addition, historical weather observations are collected from in situ weather sensors (i.e., Weather Underground, WU) within the region for the past five years. Experiments were conducted to compare the performance of the LSTM network with different training datasets: 1) IoT data alone, or 2) IoT data with the historical five years of WU data. By leveraging the historical air temperature from WU, the LSTM model achieved a generally increased accuracy by being exposed to more historical patterns that might not be present in the IoT observations. Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.INDEX TERMS air temperature, Internet of Things (IoT), long short-term memory (LSTM), urban weather

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Section: A Overall Performance Of the Predictors In Comparisonmentioning

confidence: 99%

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Yu¹,

et al. 2021

IEEE Access

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“…Lyu et al [ 9 ] first fused the gas information of multiple sensors inside the coal mine, and then used the LSTM model based on encoder-decoder to construct multivariant regression and predict the short-term gas concentration. Wu et al [ 10 ] firstly used the t-distributed stochastic neighbor embedding (t-SNE) algorithm to perform non-linear dimensionality reduction in coal mine gas-related multi-dimensional monitoring data, then extracted the spatial feature data of the monitoring data, and finally used the support vector regression (SVR) algorithm to predict the top corner gas concentration.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, in order to predict gas concentration accurately and prevent gas accidents effectively, researchers have proposed some gas prediction methods. Models of gas concentration or gas outburst forecasts are largely based on BP neural networks [6][7][8], LSTM neural networks [9], the SVR algorithm [10,11], the ELM algorithm [12], the Gaussian process regression algorithm [13], and Sensors 2021, 21, 5730 2 of 17 some other mathematical or statistical methods [14][15][16]. These methods always use the time-series data collected by the gas sensors to establish the regression prediction model of gas concentration.…”

Section: Introductionmentioning

confidence: 99%

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

Cai

Zhou

et al. 2021

Sensors

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Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.

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“…it achieved a successful performance versus other methods in the literature, specifically in the area of text translation [50]. Recently, the Encoder-Decoder long short-term memory (LSTM) has been applied for several time series forecasting tasks, such as power consumption [51], metal temperature [52], air pollutant [53] behaviour prediction [54], and gas concentration [55]. However, the LSTM core for Encoder-Decoder architecture needs to be developed using recent deep units.…”

Section: Review Of Predictions Modelsmentioning

confidence: 99%

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Shahin

Almotairi

2021

Fractal Fract

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The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion

Cited by 71 publications

References 15 publications

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

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