A VMD and FAN Based Hybrid Model for Air Quality Index Forecasting

Babu Joseph, Ankith (2024) A VMD and FAN Based Hybrid Model for Air Quality Index Forecasting. Masters thesis, Dublin, National College of Ireland.

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Abstract

The time series data of Air Quality Index (AQI) is very complex and nonstationary, therefore the forecasting and accurate prediction of AQI is challenging. This study propose a novel VMD-FAN hybrid model using Variational Mode Decomposition (VMD) for handling noise and Fourier Analysis Networks (FAN) for handling periodicity, the hybrid model is good at predicting short term time series AQI data on a air quality dataset of Taiwan. The cleaned AQI timeseries extracted from the original dataset is decomposed into individual Intrinsic Mode Functions (IMFs) using VMD and each IMF is predicted using a FAN model subsequently aggregated to form a final forecast of AQI values. The proposed hybrid model predicts the AQI of Annan district in Taiwan with a MAE, MSE, RMSE and MAPE as 0.717643, 1.352704, 1.163058, 1.495354% respectively and is better than the compared base model. The generalizability of the model is further validated with extension analysis on different cities in Taiwan. The proposed hybrid model showcase high performance and its ability to predict complex AQI data and contributes to the research in the domain.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Nagahamulla, Harshani UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	01 Sep 2025 14:40
Last Modified:	01 Sep 2025 14:40
URI:	https://norma.ncirl.ie/id/eprint/8677

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