Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction

Chemosphere ◽  
2019 ◽  
Vol 220 ◽  
pp. 486-492 ◽  
Author(s):  
Jiachen Zhao ◽  
Fang Deng ◽  
Yeyun Cai ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Fully Connected ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

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.

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

2021 ◽  
Vol 16 (4) ◽  
pp. 35-51
Author(s):  
Bingchun Liu ◽  
Xiaogang Yu ◽  
Qingshan Wang ◽  
Shijie Zhao ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Optimal Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Concentration Prediction

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

scholarly journals Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems

2019 ◽  
Vol 9 (17) ◽  
pp. 3470
Author(s):  
Nguyen Minh-Tuan ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Sequential Data ◽  
Short Term ◽  
Data Simulation ◽  
Assignment Problems ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Fully Connected

Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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