Artificial Intelligence and Machine Learning for Health Risks Prediction

Joel, Luke Oluwaseye; Doorsamy, Wesley; Paul, Babu Sena

doi:10.1007/978-3-030-70111-6_12

Cited by 2 publications

(1 citation statement)

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“…Also, in the combined dataset, the larger number of actual values available may help mitigate the impact of missing data. Additionally, having data from multiple stations may provide more robust and representative information about the underlying patterns and relationships in the data [29]. As we increased the dataset by combining three AQM stations (Ratnapark, Shankapark, and Pulchowk) data in dataset 2, the result of Table 5 shows that the model performance improved after including the metrological data.…”

Section: Resultsmentioning

confidence: 99%

Applying machine learning algorithms to estimate PM 2.5 using satellite data and metrological data

Thapa,

Devkota

2024

J. Eng. Sci.

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Air pollution, particularly fine Particulate Matter (PM 2.5), poses significant health risks and environmental challenges worldwide. Therefore, it is essential to monitor air pollution to act on it. In this study, PM 2.5 was estimated using meteorological data and Sentinel-5P air pollution data using machine learning algorithms. The Sentinel-5P data are and the meteorological data utilized are air temperature, Relative Humidity (RH), and Wind Speed (WS). The three Air Quality Monitoring (AQM) stations in Kathmandu, Nepal, were chosen as a study area for this research. The effectiveness of several machine learning methods, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), were evaluated. Both RF and XGBoost consistently performed better than SVM and KNN in terms of PM 2.5 estimation accuracy. RF got the highest R2 value of 0.80 and SVM with the lowest R2 value of 0.62 in the Sentinel-5P dataset only. The addition of meteorological data further improved the model's performance. After including metrological data in Sentinel-5P data the RF demonstrated the maximum R2 score of 0.816 and XGBoost with R2 score of 0.814. Hence, this study demonstrated machine learning algorithms can be used to estimate PM 2.5 by utilizing satellite and meteorological data, providing important information for air quality monitoring and management.

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Section: Resultsmentioning

confidence: 99%