Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

Wu, Amin; Harrou, Fouzi; Dairi, Abdelkader; Sun, Ying

doi:10.1002/cpe.7035

Cited by 10 publications

(5 citation statements)

References 73 publications

(110 reference statements)

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“…Atmospheric pollution is becoming a global problem with a harmful influence on human health and ecoecosystems [1,2]. Ground-level ozone pollution could cause substantial damage to crops, forests, and native plants.…”

Section: Background and Motivationmentioning

confidence: 99%

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A comparison of machine learning methods for ozone pollution prediction

2023

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Precise and efficient ozone ($$\hbox {O}_{3}$$ O 3 ) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high $$\hbox {O}_{3}$$ O 3 pollution levels on human health and ecosystems. However, the complexity of $$\hbox {O}_{3}$$ O 3 formation mechanisms in the troposphere presents a significant challenge in modeling $$\hbox {O}_{3}$$ O 3 accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects $$\hbox {O}_{3}$$ O 3 concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted.

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Section: Background and Motivationmentioning

confidence: 99%

“…, ǫ n ) ∼ N (0, σ 2 n I) , and f (x) ∼ N (m(X), k(X, X)) , for abbreviation, µ := m(X) and � := k(X, X)) . Without loss of generality, the m(X) is set 0 or constant, (1)…”

Section: Gpr Modelsmentioning

confidence: 99%

A comparison of machine learning methods for ozone pollution prediction

2023

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“…The input variables included six indoor air pollutants (CO 2 , PM 2.5 , HCHO, TVOCs, bacteria, and fungi) and three indoor comfort variables (temperature, relative humidity, and wind speed). With the development of machine learning, an increasing number of ANNs variants, such as convolutional neural networks (CNNs) [107,108] and recurrent neural networks (RNNs) [109,110], are being applied to IAQ research problems. As a datadriven approach, the effectiveness of an ANNs greatly depends on the training dataset.…”

Section: Machine Learning Predictive Modelsmentioning

confidence: 99%

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Dong

Goodman

Rajagopalan

2023

IJERPH

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Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

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“…Recent work in the field of pattern recognition and machine learning [5,48] has provided significant opportunities for automatic extraction of information based on big data [28,49]. This is largely driven by the deep learning wave [21], which describes the most representative and discriminative features through hierarchical multilayer neural networks.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for insight-based analytics to improve shopping experience

BOUSLIMI

2024

Preprint

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The accurate and efficient detection of shoppers and classification by age group, gender, and cart or trolley in supermarkets is essential for strategic retail planning. With the advent of deep learning algorithms, various models based on Convolutional Neural Networks (CNNs) have been proposed for object detection in high-resolution spatial images. In this article, we proposed an architecture that consists of two phases: a first phase of shopper detection where we conducted a comparative study of three well-established CNN-based models, namely Single Shot Multi-Box Detector (SSD), You Look Only Once-v8 (YOLOv8), and Faster R-CNN, to detect shoppers by age group, gender, and shopping basket. Transfer learning and fine-tuning approaches were implemented to train the models. The evaluation results for accuracy and efficiency show that YOLOv8 achieved the best performance in terms of mean Average Precision (mAP), Frames Per Second (FPS) metrics, and visual inspection. SSD demonstrated an advantage in terms of detection speed with an FPS twice as high as Faster R-CNN, although their mAP was close on the test set. The trained models were also applied to two independent test sets, proving their transferability and the importance of higher resolution images for accuracy improvement. In the second phase, we conducted a comparative study among the classification models Residual Network 50 (ResNet50), the Visual Geometry Group(VGG) 16 and 19 family, and Densely Connected Convolutional Network 121 (DenseNet121). We found a positive prediction rate of 99.28% for the VGG16 model and 98.55% for VGG19, with the others having a rate that is not too far off.

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Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

Cited by 10 publications

References 73 publications

A comparison of machine learning methods for ozone pollution prediction

A comparison of machine learning methods for ozone pollution prediction

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Deep learning for insight-based analytics to improve shopping experience

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