VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China

Dai, Hongbin; Huang, Guangqiu; Wang, Jingjing; Zeng, Huibin

doi:10.1016/j.ecoenv.2023.114960

Cited by 44 publications

(27 citation statements)

References 34 publications

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“…Some researchers introduced the VAR-XGBoost model, combining Vector Autoregression (VAR), Kriging, and XGBoost for precise, continuous O 3 concentration prediction 27 . The model was evaluated with ten-fold cross-validation and outperformed other models, including XGBoost, CatBoost, ExtraTrees, AdaBoost, RF, Decision Tree, Light Gradient Boosting Machine, etc.…”

Section: Introductionmentioning

confidence: 99%

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

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

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Section: Introductionmentioning

confidence: 99%

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Improving the accuracy of atmospheric ozone (O 3 ) pollution prediction is addressed by [40]. The authors propose a VAR-XGBoost model, which integrates vector autoregression (VAR), the Kriging method, and XGBoost (Extreme Gradient Boosting).…”

Section: Literature Reviewmentioning

confidence: 99%

Method for Delivery Planning in Urban Areas with Environmental Aspects

Lasota,

Zabielska,

Jacyna

et al. 2024

Sustainability

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This article presents research results on a multi-aspect assessment of delivery planning in the urban transport network. The distribution of goods and the operation of a network of small services in urban agglomerations is a very important decision-making problem. This is due, on one hand, to the dense development of urban areas and, on the other hand, to the increasing restrictions on minimizing harmful exhaust gases emitted by cars. Hence, many researchers are looking for decision support methods that take into account many partial points of view in the field of transport services for small service companies located in urban areas. The authors present a method of multi-criteria decision support in the planning of urban supplies, taking into account the minimization of emissions of harmful compounds (carbon dioxide, nitrogen oxides, and particulate matter), the cost, time, and amount of ecosystem exposure to these compounds. An important aspect of this research was to identify partial criteria of the decision support methodology and the definition of decision variants. The partial criteria included cost, time, and the amount of exposure of the ecosystem to harmful compounds. The Saaty method was used to determine the weights of the criteria.

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“…The results of the research are important both from the perspective of the enterprise-the economic impact and value of the enterprise, as well as the efficiency of management processes in the enterprise-and from the broader socio-economic perspective-satisfying consumer needs for goods and services, while reducing negative impacts on the environment and using its resources efficiently. In this context, attention should also be paid to the increase in air pollution caused by economic activity, which is worsening in many cities [36], and the need to take various measures to reduce it [37]. These include the creation of a good production environment and ecological production [38], as well as the reduction in negative environmental impacts, such as in dismantling lines [39].…”

Section: Introductionmentioning

confidence: 99%

An Assortment–Quantity Optimization Problem in Printing Industry Using Simulation Modelling

Smagowicz,

Szwed,

Berlec

2024

Sustainability

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This paper presents a method for assortment–quantity production scheduling in a printing company. The company uses specialized machinery to make prints on clothing. The method is based on a study of the company’s practical operations and the production technologies used. It involves the construction of simulation and optimization models of the process. The simulation models reflect the technical aspects of the production process and the business requirements. Optimization models provide solutions that balance product sales revenue with appropriate production schedules. On this basis, managers can make resource-balanced decisions on the implementation of selected production plans, taking into account the current economic conditions of the company. The experiments used the FlexSim simulation program (by FlexSim Software Products, Inc., Orem, UT 84097 USA; v. 20.1.3.1) and the OptQuest optimization package (embedded in FlexSim), resulting in a cost-effective solution in a short time. The proposed method, thanks to the optimization of the production program, provides savings in the use of materials for production, as well as water and energy savings in the production process. Thanks to the possibility of analyzing the process without interfering with it, provided by simulation modelling, the method practically eliminates the costs and time needed to prepare the execution of new production orders. This contributes to the sustainable development of the company and provides an opportunity to assess the impact of potential business decisions in the company prior to their implementation. The method has been directly applied in a company to improve its performance. The method is scalable and can be applied to problems of varying complexity and production systems of different types and sizes. This is especially true for small- and medium-sized companies that use discrete manufacturing in the textile, metal, and furniture industries.

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VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China

Cited by 44 publications

References 34 publications

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Method for Delivery Planning in Urban Areas with Environmental Aspects

An Assortment–Quantity Optimization Problem in Printing Industry Using Simulation Modelling

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