Leveraging Long Short Term Memory Neural Networks in Air Pollution Prediction
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Date
2024Author
Masinde, Augustine W
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
Air pollution is a serious environmental health hazard which contribute 6.7 Million premature
deaths worldwide annually. Studies have shown that increased exposure to air
pollution exacerbate cardiovascular and respiratory diseases such as heart failure, stroke,
chronic obstructive pulmonary disease, lung cancer and pneumonia. Major factors that
determine air pollution are PM2.5, carbon monoxide(CO), Sulphur dioxide(SO2) and Ozone(O3).
To effectively control air pollution,monitoring and forecasting is vital.
Our goal is to predict the level air pollution using Long Short Term(LSTM) Deep Neural
network algorithm. This method has emerged as a superior technique in predicting air
quality index(AQI) compared to other machine learning algorithms. We apply the LSTM
model to predict the concentration of PM2.5 in the city of Nairobi which is a major contributor
to health problems. We also quantify the effects of air pollution with the number
of new admissions of patients to hospitals with respiratory diseases.
Sensor data collected at GeoHealth Hub was split into training, validation and testing
and train the model with Adam optimization algorithm. The Root Mean Squared Error
(RMSE) was used to evaluate the model performance. We compared the performance of
the proposed model with the baseline Random forest model and the results showed that
LSTM model performed performed better than Random forest
Therefore, leveraging LSTM neural network algorithms for fitting and predicting complex
and flexible models are increasing, however not widely used in air pollution. The
study utilized this technique to obtain a complete understanding of the levels of air pollution
and also generate hypotheses for future studies that would inform the development
of strategies to mitigate the risk associated with air pollution.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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