Temporal Prediction Model for CO and CO2 Pollutants using Long Short Term Memory

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.

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A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network

Energy ◽

10.1016/j.energy.2020.118294 ◽

2020 ◽

Vol 207 ◽

pp. 118294 ◽

Cited By ~ 1

Author(s):

Wei Sun ◽

Chenchen Huang

Keyword(s):

Prediction Model ◽

Short Term Memory ◽

Carbon Price ◽

Decomposition Algorithm ◽

Short Term ◽

Term Memory ◽

Price Prediction ◽

Memory Network ◽

Long Short Term Memory

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Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Sensors ◽

10.3390/s21030941 ◽

2021 ◽

Vol 21 (3) ◽

pp. 941

Author(s):

Seongyoep Jeong ◽

Inyoung Park ◽

Hyun Soo Kim ◽

Chul Han Song ◽

Hong Kook Kim

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Image Data ◽

Short Term ◽

Temperature Prediction ◽

Term Memory ◽

Long Short Term Memory

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

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Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v3i3.1086 ◽

2019 ◽

Vol 3 (3) ◽

pp. 357-363

Author(s):

Soffa Zahara ◽

Sugianto ◽

M. Bahril Ilmiddafiq

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Optimization Algorithm ◽

Price Index ◽

Short Term Memory ◽

Wholesale Price ◽

Stochastic Gradient Descent ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

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