Weather condition-based hybrid models for multiple air pollutants forecasting and minimisation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144284" target="_blank" >RIV/00216305:26210/22:PU144284 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0959652622012276" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0959652622012276</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2022.131610" target="_blank" >10.1016/j.jclepro.2022.131610</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Weather condition-based hybrid models for multiple air pollutants forecasting and minimisation

Popis výsledku v původním jazyce

With the deterioration of air quality in recent years, the establishment of accurate and efficient forecasting models for pollutants has become the top priority. Due to the imperfect internal mechanism, the traditional numerical model, Weather Research and Forecast - Community Multiscale Air Quality (WRF-CMAQ), whose performance is limited in predicting the concentration of pollutants. To solve that issue presented, this study proposed two hybrid models for pollutant concentration forecasting based on weather conditions of various monitoring points. The hybrid model I applies long and short-term memory neural networks (LSTM) to extract the temporal characteristics and random forest (RF) to extract the non-line characteristics. Then, a fusion layer is built to combine them, which is optimised by the particle swarm optimisation (PSO) algorithm. Based on hybrid model I, hybrid model II also considers the regional synergy of different monitoring points to capture the spatial correlation of weather conditions. Taking a certain region of China as an example, the performance of these two hybrid models is proved. The results and discussions indicate that not only do the hybrid models achieve higher accuracy than other comparable models such as LSTM, convolutional neural network (CNN), and WRF-CMAQ, but they also prove that the regional synergy can significantly improve the effectiveness of air pollutants forecasting. The root mean squared error (RMSE) of the hybrid model II for predicted six pollutants concentration dropped to 1.781, 6.630, 5.556, 4.154, 49.558, 4.074 compared with the RMSE values of the hybrid model I and WRF-CMAQ, which are 1.972, 6.734, 6.731, 4.937, 63.487, 5.422 and 7.98, 38.175, 29.511, 21.077, 78.479, 22.810. This work provides the high-precision prediction and comprehensive evaluation of primary pollutants, which provides a targeting option to deal with the highest predicted pollutants.

Název v anglickém jazyce

Weather condition-based hybrid models for multiple air pollutants forecasting and minimisation

Popis výsledku anglicky

With the deterioration of air quality in recent years, the establishment of accurate and efficient forecasting models for pollutants has become the top priority. Due to the imperfect internal mechanism, the traditional numerical model, Weather Research and Forecast - Community Multiscale Air Quality (WRF-CMAQ), whose performance is limited in predicting the concentration of pollutants. To solve that issue presented, this study proposed two hybrid models for pollutant concentration forecasting based on weather conditions of various monitoring points. The hybrid model I applies long and short-term memory neural networks (LSTM) to extract the temporal characteristics and random forest (RF) to extract the non-line characteristics. Then, a fusion layer is built to combine them, which is optimised by the particle swarm optimisation (PSO) algorithm. Based on hybrid model I, hybrid model II also considers the regional synergy of different monitoring points to capture the spatial correlation of weather conditions. Taking a certain region of China as an example, the performance of these two hybrid models is proved. The results and discussions indicate that not only do the hybrid models achieve higher accuracy than other comparable models such as LSTM, convolutional neural network (CNN), and WRF-CMAQ, but they also prove that the regional synergy can significantly improve the effectiveness of air pollutants forecasting. The root mean squared error (RMSE) of the hybrid model II for predicted six pollutants concentration dropped to 1.781, 6.630, 5.556, 4.154, 49.558, 4.074 compared with the RMSE values of the hybrid model I and WRF-CMAQ, which are 1.972, 6.734, 6.731, 4.937, 63.487, 5.422 and 7.98, 38.175, 29.511, 21.077, 78.479, 22.810. This work provides the high-precision prediction and comprehensive evaluation of primary pollutants, which provides a targeting option to deal with the highest predicted pollutants.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Svazek periodika

neuveden

Číslo periodika v rámci svazku

352

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

131610-131610

Kód UT WoS článku

000793460700002

EID výsledku v databázi Scopus

2-s2.0-85127499950