Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Toxics, Journal Year: 2023, Volume and Issue: 11(1), P. 51 - 51

Published: Jan. 3, 2023

Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) wavelet-ANNs (WANNs) are used predict in Shanghai. Shanghai from 2014 2020 decreased by 39.3%. serious COVID-19 epidemic had an unprecedented effect on during the lockdown is significantly reduced compared period before lockdown. First, correlation analysis utilized identify associations between meteorological elements Second, estimating twelve training algorithms twenty-one network structures these models, results show that optimal input predicting models were 3 previous days fourteen elements. Finally, activation function (tansig-purelin) ANNs WANNs better than others training, validation forecasting stages. Considering coefficients (R) next day influence factors, showed closest relation with 1 lag closer relationships minimum atmospheric temperature, maximum pressure, 2 lag. When Bayesian regularization (trainbr) was train, ANN WANN precisely simulated calibration It emphasized WANN1 model obtained terms R (0.9316). These prove adept because they output factors. Therefore, our research offer a theoretical basis air pollution control.

Language: Английский

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Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data

Chemosphere, Journal Year: 2023, Volume and Issue: 340, P. 139886 - 139886

Published: Aug. 21, 2023

Language: Английский

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Monthly climate prediction using deep convolutional neural network and long short-term memory

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 31, 2024

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include neural network (ANN), recurrent NN (RNN), long short-term memory (LSTM), deep convolutional (CNN), CNN-LSTM. These used forecast six climatic factors on a monthly ahead. data for 72 years (1 January 1951–31 December 2022) this study average atmospheric temperature, extreme minimum maximum precipitation, relative humidity, sunlight hours. time series 12 month delayed as input signals the models. efficiency examined utilizing diverse evaluation criteria namely mean absolute error, root square error (RMSE), correlation coefficient (R). modeling result inherits that hybrid CNN-LSTM model achieves greater accuracy than other compared significantly reduces forecasting one step For instance, RMSE values ANN, RNN, LSTM, CNN, temperature stage 2.0669, 1.4416, 1.3482, 0.8015 0.6292 °C, respectively. findings simulations shows potential improve forecasting. prediction will contribute meteorological disaster prevention reduction, well flood control drought resistance.

Language: Английский

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The Characteristics of Air Quality Changes in Hohhot City in China and their Relationship with Meteorological and Socio-economic Factors

Aerosol and Air Quality Research, Journal Year: 2024, Volume and Issue: 24(5), P. 230274 - 230274

Published: Jan. 1, 2024

Air pollution affects sustainable development of the natural environment and social economy. In this article, changes in air quality index (AQI) six pollutants Hohhot during 2014–2022 are analyzed. The results imply that annual average concentrations five (SO2, PM10, PM2.5, NO2, CO) AQI values declined year by over 2014–2022. Compared with 2014, fell 22.5% 2022. However, O3 increased year. PM2.5 PM10 were major factors influencing AQI. Among types atmospheric pollutants, relationship between NO2 is strongest, implying plays a significant influence formation PM2.5. Meteorological socio-economic have impact on quality. wind speed (AWS), pressure (AP), sulfur dioxide emissions (SOE), nitrogen oxide (NOE), particulate matter (PME) positive effect provide information great importance for management City.

Language: Английский

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Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 25, 2025

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization urbanization, Liaocheng has experienced increasing ozone concentration over several years. Therefore, become a major environmental problem in City. Long short-term memory (LSTM) artificial neural network (ANN) models are established predict concentrations City from 2014 2023. The results show general improvement accuracy LSTM model compared ANN model. Compared ANN, an increase determination coefficient (R2), value 0.6779 0.6939, decrease root mean square error (RMSE) 27.9895 μg/m3 27.2140 absolute (MAE) 21.6919 20.8825 μg/m3. prediction is superior terms R, RMSE, MAE. In summary, promising technique for predicting concentrations. Moreover, by leveraging historical data enables accurate predictions future on global scale. This will open up new avenues controlling mitigating pollution.

Language: Английский

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Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network

Atmosphere, Journal Year: 2022, Volume and Issue: 13(8), P. 1221 - 1221

Published: Aug. 2, 2022

Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective study to predict concentration employing an artificial neural network (ANN). annual in Liaocheng from 2014 2021 shows a gradual decreasing trend. air quality during lockdown after periods 2020 was obviously improved compared with same 2019. ANN employed contains hidden layer 6 neurons, input 11 parameters, output layer. First, used 80% data training, then 10% verification. value correlation coefficient (R) training validation 0.9472 0.9834, respectively. In forecast period, it demonstrated that model Bayesian regularization (BR) algorithm (trainbr) obtained best forecasting performance terms R (0.9570), mean absolute error (4.6 μg/m3), root square (6.6 has produced accurate results. These results prove effective monthly predicting due fact can identify nonlinear relationships between variables.

Language: Английский

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Status of Air Pollution during COVID-19-Induced Lockdown in Delhi, India

Atmosphere, Journal Year: 2022, Volume and Issue: 13(12), P. 2090 - 2090

Published: Dec. 12, 2022

To monitor the spread of novel coronavirus (COVID-19), India, during last week March 2020, imposed national restrictions on movement its citizens (lockdown). Although India’s economy was shut down due to restrictions, nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading pollution, especially urban and industrial areas. this paper, we explored linkages between air quality nationwide lockdown city using geographic information system (GIS)-based approach. Data from 37 stations were monitored 12 March, 2020 2 April, it found that Air Quality Index for almost reduced by 37% 46% concerning PM2.5 PM10, respectively. The study highlights that, regular conditions, atmosphere’s natural healing rate against anthropogenic activities is lower, as indicated higher AQI. However, lockdown, sudden cessation leads period which greater than induced disturbances, resulting lower AQI, thus proving pandemic given small window environment breathe helped districts recover serious issues related bad quality. If such windows are incorporated into policy decision-making, these can prove be effective measures controlling pollution heavily polluted regions World.

Language: Английский

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Change in Air Quality during 2014–2021 in Jinan City in China and Its Influencing Factors

Toxics, Journal Year: 2023, Volume and Issue: 11(3), P. 210 - 210

Published: Feb. 24, 2023

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) concentrations of six pollutants Jinan during 2014–2021. The results indicate that annual average PM10, PM2.5, NO2, SO2, CO, O3 AQI values all declined year after Compared with 2014, City fell by 27.3% 2021. four seasons 2021 was obviously better than 2014. PM2.5 concentration highest winter lowest summer, while it opposite for concentration. COVID epoch 2020 remarkably lower compared same Nevertheless, post-COVID conspicuously deteriorated Socioeconomic elements were main reasons quality. majorly influenced energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx (NOE), particulate (PE), PM10. Clean policies played a key role improving Unfavorable meteorological conditions led to heavy weather winter. These could provide scientific reference control City.

Language: Английский

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A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2870 - 2870

Published: Oct. 9, 2024

Climate change affects the water cycle, resource management, and sustainable socio-economic development. In order to accurately predict climate in Weifang City, China, this study utilizes multiple data-driven deep learning models. The data for 73 years include monthly average air temperature (MAAT), minimum (MAMINAT), maximum (MAMAXAT), total precipitation (MP). different models artificial neural network (ANN), recurrent NN (RNN), gate unit (GRU), long short-term memory (LSTM), convolutional (CNN), hybrid CNN-GRU, CNN-LSTM, CNN-LSTM-GRU. CNN-LSTM-GRU MAAT prediction is best-performing model compared other with highest correlation coefficient (R = 0.9879) lowest root mean square error (RMSE 1.5347) absolute (MAE 1.1830). These results indicate that method a suitable model. This can also be used surface modeling. will help flood control management.

Language: Английский

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Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Frontiers in Forests and Global Change, Journal Year: 2023, Volume and Issue: 6

Published: Dec. 8, 2023

Introduction Atmospheric temperature affects the growth and development of plants has an important impact on sustainable forest ecological systems. Predicting atmospheric is crucial for management planning. Methods Artificial neural network (ANN) deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional (CNN), CNN-GRU, CNN-LSTM, were utilized to predict change monthly average extreme temperatures in Zhengzhou City. Average data from 1951 2022 divided into training sets (1951–2000) prediction (2001–2022), 22 months used model input next month. Results Discussion The number neurons hidden layer was 14. Six different algorithms, along with 13 various functions, trained compared. ANN evaluated terms correlation coefficient (R), root mean square error (RMSE), absolute (MAE), good results obtained. Bayesian regularization (trainbr) best performing algorithm predicting average, minimum maximum compared other algorithms R (0.9952, 0.9899, 0.9721), showed lowest values RMSE (0.9432, 1.4034, 2.0505), MAE (0.7204, 1.0787, 1.6224). CNN-LSTM performance. This method had generalization ability could be forecast areas. Future climate changes projected using model. temperature, 2030 predicted 17.23 °C, −5.06 42.44 whereas those 2040 17.36 −3.74 42.68 respectively. These suggest that continue warming future.

Language: Английский

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