Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

Yafouz, Ayman; AlDahoul, Nouar; Birima, Ahmed H.; Ahmed, Ali Najah; Sherif, Mohsen; Sefelnasr, Ahmed; Allawi, Mohammed Falah; El‐Shafie, Ahmed

doi:10.1016/j.aej.2021.10.021

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…Here, information on air pollutants and pollution is made available in processed form through various mathematical calculations. A common feature of these models is machine learning or artificial intelligence, on which the prediction calculations conducted are based [ 89 – 92 ]. The prediction of air quality and pollution within these models thereby includes different pollutants, either analyzing only a single pollutant, like O 3 [ 89 ] or NO 2 [ 91 ] or different pollutants in parallel, for example PM 10 , SO 2 , CO, NO 2 and O 3 [ 90 , 92 ].…”

Section: Resultsmentioning

confidence: 99%

“…A common feature of these models is machine learning or artificial intelligence, on which the prediction calculations conducted are based [89][90][91][92]. The prediction of air quality and pollution within these models thereby includes different pollutants, either analyzing only a single pollutant, like O 3 [89] or NO 2 [91] or different pollutants in parallel, for example PM 10 , SO 2 , CO, NO 2 and O 3 [90,92]. In this context, Wu and Lin [92] optimized a model that includes the air-quality index (AQI), a summary value based on different air pollutants that evaluates air quality and can provide accurate information reflected in a single value.…”

Section: Prediction Modelsmentioning

confidence: 99%

“…The models differ slightly based on their intended use. Some models are used to monitor current air quality [89], while others are used to forecast air pollution over time [90,91]. In addition, Yan et al [90] also mentioned warning the public as an intended goal of these systems.…”

Section: Prediction Modelsmentioning

confidence: 99%

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Science communication on the public health risks of air pollution: a computational scoping review from 1958 to 2022

et al. 2023

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Background Air pollutants are a health risk for the entire population. Particulate matter (PM) including the smallest fraction, ultra-fine particles (UFP), therefore continue to be the focus of scientific research in this area. To protect the population from the harmful effects of exposure to PM, communication and information of research results are of special relevance as individuals with heightened awareness of the harms of poor air quality are more likely to take action to improve their exposure. Methods We conducted a scoping review of the scientific literature on science communication of public health information about risks associated with air pollutants to generate an initial over-view of existing research in this field. We searched the PubMed and Scopus databases and analyzed the data using a structured topic modeling (STM) approach. Results The existing scientific literature dates back to 1958 but increases significantly from the 1990s onwards. Publications are mainly found in the discipline of environmental research and are primarily concerned with health effects. It is often stated that adequate communication of the results to the public would be important, but specific approaches are rare. Overall, the topic of risk communication seems to be underrepresented for both air pollutants and UFP. Conclusions To protect public health, it is important to conduct more intensive science and risk communication related to scientific findings on the risks of air pollutants. For adequate communication and information, further research is needed to provide specific approaches that also involve the affected population and take different target groups into account. In addition, the effectiveness of communication efforts should also be analyzed.

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Section: Resultsmentioning

confidence: 99%

Section: Prediction Modelsmentioning

confidence: 99%

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Science communication on the public health risks of air pollution: a computational scoping review from 1958 to 2022

et al. 2023

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“…In order to evaluate the pollution degree of the environment, the concentration of factors that caused pollution must be identified [ 17 , 18 , 19 ]. The groundwater quality index (GWQI) and irrigation water quality index (IWQI) can evaluate groundwater quality for drinking and irrigation purposes [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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“…The existing ozone concentration prediction methods can be roughly divided into three directions. One is to optimize traditional machine learning algorithms and use various optimization methods to learn complex patterns and hidden features [1] . There are two main ideas: the Bayesian method and the kernel method.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

Zhang

Chen

Liu

et al. 2022

Heliyon

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Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

Cited by 20 publications

References 43 publications

Science communication on the public health risks of air pollution: a computational scoping review from 1958 to 2022

Science communication on the public health risks of air pollution: a computational scoping review from 1958 to 2022

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

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