Enhancing PM2.5 Predictions in Dakar Through Automated Data Integration into a Data Assimilation Model DOI
Ahmed Gueye, Mamadou Simina Dramé, Serigne Abdoul Aziz Niang

et al.

Aerosol Science and Engineering, Journal Year: 2024, Volume and Issue: 8(4), P. 402 - 413

Published: May 15, 2024

Language: Английский

Prospective technical and technological insights into microalgae production using aquaculture wastewater effluents DOI Creative Commons

Ira-Adeline Simionov,

Marian Barbu, Iulian Vasiliev

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124537 - 124537

Published: Feb. 27, 2025

Microalgae biomass is a promising resource addressing climate change and play role in energy transition for generating biofuels. Due to their ability produce higher yield per year, biofuels obtained from microalgae are considered 3rd generation-advanced The industrial production of mitigates the effects CO2 emissions can be used wastewater bioremediation since most effluents rich nutrients. Using as growth media promotes principles circular economy nutrient recovery. aquaculture effluent contains high levels nitrogenous compounds, well phosphates dissolved organic carbon. current review aims identify, centralize, provide extensive information on decisive technological technical factors involved process different species wastewater. study focuses performance indicators, specific control strategies applied achieve pH control, it has been highlighted one important growth-related cofactors. A bibliometric framework was developed identify future trends integrated production. scientific literature analysis great potential production, due superior lipid carbohydrate productivity. Most systems found aim at controlling bioreactor by injecting CO2, while few other papers consider manipulating oxygen. need higher-level arises not only track or DO references but also maximize treatment efficiency bioreactor.

Language: Английский

Citations

0
PM2.5 probabilistic forecasting system based on graph generative network with graph U-nets architecture DOI
Yanfei Li, Rui Yang, Zhu Duan

et al.

Journal of Central South University, Journal Year: 2025, Volume and Issue: 32(1), P. 304 - 318

Published: Jan. 1, 2025

Language: Английский

Citations

0
A novel cryptocurrency price time series hybrid prediction model via machine learning with MATLAB/Simulink DOI
Lingxiao Zhao,

Zhiyang Li,

Yue Ma

et al.

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 79(14), P. 15358 - 15389

Published: April 17, 2023

Language: Английский

Citations

8
Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation DOI Creative Commons
Ahmed Gueye, Mamadou Simina Dramé,

S. Niang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 22, 2024

Abstract The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server assimilation model. Initially, 3-year set was used identify and validate appropriate ARIMA split 80% training 20% test set. Augmented Dickey-Fuller (ADF) check the normality series. Subsequently, we AutoArima method determine optimal model represent time Preliminary results show that with order (2,1,1) accurately represents Additional analysis fit tests showed (3, 0, 1) most effective representing predicting data. statistical validation performance demonstrates its capability forecast concentrations for up 72 hours (3 days), achieving correlation coefficients exceeding 80%. However, after three days, predictions returned background levels. In final stage study, automatic stations were hosting improve forecasts Dakar. An interactive platform developed visualize measurements over two days. by integrating model, are significantly improved.

Language: Английский

Citations

2
Analysis of chemical production accidents in China: data mining, network modeling, and predictive trends DOI Creative Commons
Yang Shi, Haitao Bian, Qingguo Wang

et al.

Emergency Management Science and Technology, Journal Year: 2024, Volume and Issue: 4(1), P. 0 - 0

Published: Jan. 1, 2024

In recent years, China has experienced frequent chemical production accidents. This study collates 1900 reports of such incidents from 2012 to 2023, gathered multiple sources. By employing association rule mining, a data mining technique, we analyzed the relationships between causative factors these accidents and their patterns. analysis revealed significant rules characterized by high lift values, severe consequences, or previously under recognized Utilizing Gephi® software, constructed network model representing Through centrality nodes, identified key contributing incidents. Additionally, developed validated SARIMAX using time series accidents, enabling predictions about future trends. The model's forecasts offer valuable insights for businesses in identifying periods with higher likelihood Conclusively, this comprehensive predictive modeling provide critical framework enhancing safety measures proactive risk management China's industry.

Language: Английский

Citations

2
Air quality prediction using a novel three-stage model based on time series decomposition DOI
Mingyue Sun, Congjun Rao, Zhuo Hu

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Language: Английский

Citations

2
Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction DOI Open Access
Lan Wang, Nan Li, Ming Xie

et al.

Nonlinear Dynamics, Journal Year: 2023, Volume and Issue: 111(9), P. 8571 - 8590

Published: Feb. 25, 2023

Language: Английский

Citations

6
Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer DOI Creative Commons

Jiahui Duan,

Yaping Gong,

Jun Luo

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 30, 2023

Abstract The air pollution problem seriously affects economic development and people's health, an efficient accurate forecasting model of quality will help manage problems. comparison models chosen by other scholars are often based on derivative the proposed do not comprehensively compare types with limited accuracy. In this paper, we establishes a combined ARIMA-CNN-LSTM to predict index accurately. mainly consists two parts: Using ARIMA fit linear part data using CNN-LSTM nonlinear avoid blindness in hyperparameter setting. To dilemma setting, article uses Dung Beetle Optimizer tool find hyperparameters model, determine best hyperparameters, check accuracy model. is compared widely used models. results show that can effectively search for optimal solve setting And optimized ARIMA-DBO-CNN-LSTM has higher predictive stronger adaptability predicting three cities.

Language: Английский

Citations

6
Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model DOI Creative Commons
Tanattrin Bunnag

International Journal of Energy Economics and Policy, Journal Year: 2024, Volume and Issue: 14(1), P. 418 - 426

Published: Jan. 15, 2024

This paper analyzes the relationship between air pollutants and amount of PM10 measured in Bangkok. It forecasts Bangkok by using SARIMA SARIMA-GARCH models to formulate policies reduce occurrence guidelines for further prevention. PM's data is from January 2008 July 2023. First, process build Model Estimation. We perform model comparisons that (3,1,3)(1,1,2)12 SARIMA(3,1,3)(1,1,2)12-GARCH(1,1), which gives lower MAE RMSE values, indicates good prediction accuracy than another model. The results show predictions (3,1,3) (1,1,2)12 are 15.303 20.839 better those (1,1,2)12-GARCH (1,1) 17.280 22.677. Therefore, forecast precise. Thus, summary, we will choose first use forecasting policy making. Moreover, study results, found elements NO2 O3 require quite a lot attention because they affect with at moderate level.

Language: Английский

Citations

1
Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height DOI
Lingxiao Zhao,

Zhiyang Li,

Yuguo Pei

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 226, P. 120345 - 120345

Published: March 16, 2024

Language: Английский

Citations

1