PM10 density forecast model using long short term memory

Park, Jung Hwan; Yoo, Seong-Joon; Kim, Kyung Joong; Gu, Yeonghyeon; Lee, Keon-Hoon; Son, U-Hyon

doi:10.1109/icufn.2017.7993855

Cited by 16 publications

(12 citation statements)

References 4 publications

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“…Todorov et al evaluated different stochastic approaches to evaluate the sensitivity indexes, allowing a comparison to be made between the input parameters with respect to their influence on the points of interest [48]. To evaluate the results of the time series analysis we used the root mean square error (RMSE) [49] and the correlation coefficient (CC) [50]. The variable "M" represents the values of the records modeled by the recurrent networks (Elman, LSTM, and GRU), the variable "R" is the actual records, and "n" the number of total data.…”

Section: Model Evaluationmentioning

confidence: 99%

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

et al. 2021

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Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM10 and PM2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics.

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Section: Model Evaluationmentioning

confidence: 99%

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

et al. 2021

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“…Some other techniques include using a 30-day moving average to detect and remove noisy data (J. Park et al, 2017), wavelet transformation (Shekarrizfard et al, 2012), or replacing by maximum and minimum value (Sabri & Tarek, 2012). Outlier data, extreme or abnormal data, were also found to be detected, transformed or eliminated by statistical methods.…”

Section: Data Cleaningmentioning

confidence: 99%

“…Díaz-Robles et al (2008); J. Park et al (2017) proposed the Cook's distance, the difference in fits, and the standardized difference of the beta to deal with outlier data.…”

Section: Data Cleaningmentioning

confidence: 99%

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Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic

Srijiranon

Eiamkanitchat

Ramingwong

et al. 2021

WIREs Data Min & Knowl

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Coarse particulate matter (PM10), the inhalable particles with an aerodynamic diameter smaller than 10 micrometers are one of the major air pollutions that affect human health. Over the previous decade, a number of researchers applied various data mining techniques to create a temporal prediction model. This study reviews and discusses 100 research articles in computer science and environmental science coming from the Scopus database. The three processes of data mining techniques, including data preparation, model creation, and model evaluation for prediction PM10 are highlighted. A summary of the overall process directions of data mining as well as their output are revealed. Additionally, recommendations for future research are identified.This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Prediction

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“…The rule-based classification is used from a combination of two models, ANN and K-nearest neighbor (K-NN), to improve model performance in the minor classes. There is another type of ANN, long short-term memory (LSTM), used by [25]. The research presented an appropriate LSTM structure to predict the daily average PM 10 concentration in Seoul, South Korea.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Srijiranon

Eiamkanitchat

2021

Applied Sciences

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Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

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PM10 density forecast model using long short term memory

Cited by 16 publications

References 4 publications

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic

Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

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PM10 density forecast model using long short term memory

Cited by 16 publications

References 4 publications

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach

Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Contact Info

Product

Resources

About

Investigation of PM₁₀ prediction utilizing data mining techniques: Analyze by topic