Journal
REMOTE SENSING
Volume 13, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/rs13163222
Keywords
asthma; spatial modeling; ensemble machine learning; remote sensing (RS); geographic information system (GIS)
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Funding
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2021-2016-0-00312]
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This study utilized three ensemble machine learning algorithms to model asthma-prone areas in Tehran, Iran. Factors influencing asthma occurrence, such as distance to the street, NDVI, and traffic volume, were identified using spatial databases and remote sensing imagery. The AdaBoost algorithm outperformed Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O-3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O-3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
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