scholarly journals Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1500
Author(s):  
Dimitris Akritidis ◽  
Prodromos Zanis ◽  
Aristeidis K. Georgoulias ◽  
Eleni Papakosta ◽  
Paraskevi Tzoumaka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Urban Air Quality ◽  
Gradient Boosting ◽  
Urban Air ◽  
Extreme Gradient Boosting ◽  
The Impact

Following the rapid spread of COVID-19, a lockdown was imposed in Thessaloniki, Greece, resulting in an abrupt reduction of human activities. To unravel the impact of restrictions on the urban air quality of Thessaloniki, NO2 and O3 observations are compared against the business-as-usual (BAU) concentrations for the lockdown period. BAU conditions are modeled, applying the XGBoost (eXtreme Gradient Boosting) machine learning algorithm on air quality and meteorological surface measurements, and reanalysis data. A reduction in NO2 concentrations is found during the lockdown period due to the restriction policies at both AGSOFIA and EGNATIA stations of −24.9 [−26.6, −23.2]% and −18.4 [−19.6, −17.1]%, respectively. A reverse effect is revealed for O3 concentrations at AGSOFIA with an increase of 12.7 [10.8, 14.8]%, reflecting the reduced O3 titration by NOx. The implications of COVID-19 lockdowns in the urban air quality of Thessaloniki are in line with the results of several recent studies for other urban areas around the world, highlighting the necessity of more sophisticated emission control strategies for urban air quality management.

Assessing the impact of the New Athens airport to urban air quality with contemporary air pollution models

1997 ◽  
Vol 31 (10) ◽  
pp. 1497-1511 ◽  
Author(s):  
N. Moussiopoulos ◽  
P. Sahm ◽  
K. Karatzas ◽  
S. Papalexiou ◽  
A. Karagiannidis
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
The Impact ◽  
Air Pollution Models

scholarly journals Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arturo Moncada-Torres ◽  
Marissa C. van Maaren ◽  
Mathijs P. Hendriks ◽  
Sabine Siesling ◽  
Gijs Geleijnse
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Explicit Knowledge ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Gradient Boosting ◽  
Support Vector ◽  
Netherlands Cancer Registry ◽  
Extreme Gradient Boosting ◽  
The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

scholarly journals Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

2021 ◽  
Vol 21 (9) ◽  
pp. 7373-7394
Author(s):  
Jérôme Barré ◽  
Hervé Petetin ◽  
Augustin Colette ◽  
Marc Guevara ◽  
Vincent-Henri Peuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Gradient Boosting ◽  
Quality Models ◽  
Pollution Levels ◽  
Surface Station ◽  
Concentration Changes ◽  
Monitoring Service ◽  
No2 Pollution

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

scholarly journals Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach

2022 ◽  
Vol 17 (1) ◽  
pp. 165-198
Author(s):  
Kamil Matuszelański ◽  
Katarzyna Kopczewska
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Latent Dirichlet Allocation ◽  
Demographic Data ◽  
Numerical Data ◽  
Gradient Boosting ◽  
Modern Approach ◽  
Rural And Urban Areas ◽  
Customer Churn ◽  
Extreme Gradient Boosting

This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers’ propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers’ propensity to churn is not influenced by: (i) population density in the customer’s area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

scholarly journals Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1449 ◽  
Author(s):  
Yulong Shi ◽  
Yang Zhang ◽  
Hans-Arno Jacobsen ◽  
Lulu Tang ◽  
Geoffrey Elliott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Loss Rate ◽  
Differentiated Services ◽  
Gradient Boosting ◽  
Queuing Delay ◽  
End To End Delay ◽  
Extreme Gradient Boosting ◽  
Real Value ◽  
Difficult Issue

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

scholarly journals Citizen as Sensors' Commitment in Urban Public Action

2019 ◽  
Vol 8 (4) ◽  
pp. 42-59 ◽  
Author(s):  
Gwendoline l'Her ◽  
Myriam Servières ◽  
Daniel Siret
Keyword(s):  
Air Quality ◽  
Citizen Science ◽  
Urban Air Quality ◽  
Urban Air ◽  
The Public ◽  
Public Action ◽  
Quality Issue ◽  
Data Analyses ◽  
The Impact

Based on a case study in Rennes, the article presents how a group of urban public actors re-uses methods and technology from citizen sciences to raise the urban air quality issue in the public debate. The project gives a group of inhabitants the opportunity to follow air quality training and proceed PM2.5µm measurements. The authors question the impact of the ongoing hybridisation between citizen science and urban public action on participants' commitment. The authors present how the use of PM2.5-sensors during 11 weeks led to a disengagement phenomenon, even if the authors observe a strong participation to workshops. These results come from an interdisciplinary methodology using observations, interviews, and data analyses.

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