A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network DOI Open Access
Zhong Huang, Linna Li, Guorong Ding

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10660 - 10660

Published: July 6, 2023

Precise and efficient air quality prediction plays a vital role in safeguarding public health informing policy-making. Fine particulate matter, specifically PM2.5 PM10, serves as crucial indicator for assessing managing pollution levels. In this paper, daily concentration model combining successive variational mode decomposition (SVMD) bidirectional long short-term memory (BiLSTM) neural network is proposed. Firstly, SVMD used an unsupervised feature-learning method to divide data into intrinsic functions (IMFs) extract frequency features improve trend prediction. Secondly, the BiLSTM introduced supervised learning capture small changes pollutant sequence perform of decomposed sequence. Furthermore, Bayesian optimization (BO) algorithm employed identify optimal key parameters model. Lastly, predicted values are reconstructed generate final results PM10 datasets. The performance proposed validated using datasets collected from China Environmental Monitoring Center Tianshui, Gansu, Wuhan, Hubei. show that can smooth original series more effectively than other methods, BO-BiLSTM better LSTM-based models, thereby proving has excellent feasibility accuracy.

Language: Английский

Optimized machine learning model for air quality index prediction in major cities in India DOI Creative Commons

Suresh Kumar Natarajan,

Prakash Shanmurthy,

A. Daniel

et al.

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

Published: March 21, 2024

Abstract Industrial advancements and utilization of large amount fossil fuels, vehicle pollution, other calamities increases the Air Quality Index (AQI) major cities in a drastic manner. Major AQI analysis is essential so that government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence prediction based on pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with Decision Tree (DT) algorithm for accurate India. quality data available Kaggle repository used experimentation, like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, Chennai are considered analysis. The proposed performance experimentally verified through metrics R-Square, RMSE, MSE, MAE, accuracy. Existing models, k-nearest Neighbor, Random Forest regressor, Support vector compared model. attains better traditional algorithms maximum accuracy 88.98% New Delhi city, 91.49% Bangalore 94.48% 97.66% 95.22% 97.68% Visakhapatnam city.

Language: Английский

Citations

23
Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2.5 concentration long-term prediction DOI
Qinghe Zheng, Xinyu Tian, Zhiguo Yu

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 245, P. 123008 - 123008

Published: Dec. 29, 2023

Language: Английский

Citations

33
Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition DOI
Xihai Zhang, Xianghui Chen,

Guochen Zheng

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 221, P. 115259 - 115259

Published: Jan. 10, 2023

Language: Английский

Citations

30
Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning DOI Open Access
Mihaela Tinca Udriștioiu, Youness El Mghouchi, Hasan Yıldızhan

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 421, P. 138496 - 138496

Published: Aug. 17, 2023

Language: Английский

Citations

27
Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model DOI
Jinxing Shen, Q. Liu, Xuejun Feng

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 370, P. 122703 - 122703

Published: Oct. 1, 2024

Language: Английский

Citations

9
Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction DOI
Yiman Li, Peng Tian, Lei Hua

et al.

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 87, P. 104209 - 104209

Published: Sept. 25, 2022

Language: Английский

Citations

36
Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm DOI Creative Commons
Adil Masood, Mohammed Majeed Hameed, Aman Srivastava

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 29, 2023

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In context, accurate prediction of PM2.5 concentration critical for raising public awareness, allowing sensitive populations to plan ahead, providing governments with information alerts. This study applies novel hybridization extreme learning machine (ELM) snake optimization algorithm called ELM-SO model forecast concentrations. The has been developed on quality inputs meteorological parameters. Furthermore, hybrid compared individual models, Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, deep known Long Short-Term Memory networks (LSTM), forecasting results suggested exhibited highest level predictive performance among five testing value squared correlation coefficient (R2) 0.928, root mean square error 30.325 µg/m3. study's findings suggest technique valuable tool accurately concentrations could help advance field forecasting. By developing state-of-the-art pollution models incorporate ELM-SO, it may be possible understand better anticipate effects human environment.

Language: Английский

Citations

19
A novel hybrid model for hourly PM2.5 prediction considering air pollution factors, meteorological parameters and GNSS-ZTD DOI Creative Commons

Fanming Wu,

Pengfei Min,

Yan Jin

et al.

Environmental Modelling & Software, Journal Year: 2023, Volume and Issue: 167, P. 105780 - 105780

Published: July 31, 2023

Language: Английский

Citations

17
MGAtt-LSTM: A multi-scale spatial correlation prediction model of PM2.5 concentration based on multi-graph attention DOI
Bo Zhang, Weihong Chen, Maozhen Li

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 179, P. 106095 - 106095

Published: June 7, 2024

Language: Английский

Citations

7
Experimental and Techno-Economic Analysis of Solar PV System for Sustainable Building and Greenhouse Gas Emission Mitigation in Harsh Climate: A Case Study of Aswan Educational Building DOI Open Access

Esraa M. Abd Elsadek,

Hossam Kotb, Ayman S. Abdel‐Khalik

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(13), P. 5315 - 5315

Published: June 21, 2024

Climate change is a global issue that requires collective action to address. One of the most pressing concerns reducing emissions resulting from combustion processes. The use renewable energy sources and green has become trend worldwide. Solar one promising due its abundance simplicity implementation. city Aswan, located in South Egypt, high solar radiation makes it ideal for utilizing power. current study investigates optimal design sustainable building electricity system at Aswan Campus Arab Academy Science, Technology & Maritime Transport (AASTMT) Egypt. campus four sources: utility grid, PV panels, batteries, diesel generator, along with weather station. Experimental investigations have been carried out this research paper performance characteristics Moreover, HOMER pro software used model various configurations including different photovoltaic (PV) panel types tracking systems. simulations are compared real-world data collected station on campus. Additionally, CO2 NO2 measured assess environmental impact scenarios. total net cost over life cycle also calculated cases. results demonstrate addition can reduce traditional grid usage by 38% 50%. A decrease Levelized Cost Energy (LOCE) USD 0.0647 0.0535 reported. difference NCP between dual-axis fixed zero angle 143,488. dual degree tracker panels further enhance production 30% more, while carbon dioxide more than 20%. simulation reveal systems provide greater generation, cost–benefit analysis may prioritize some closely match those experimental data, which presentation error does not exceed 8%, demonstrating software’s effectiveness optimizing This demonstrates comprehensive optimization building’s significantly costs, lower emissions, promote energy, particularly

Language: Английский

Citations

6