Cluster Analysis of Haze Episodes Based on Topological Features
| Autor: | Mohd Almie Alias, Fatimah Abdul Razak, Mohd Salmi Md Noorani, Munira Ismail, Nur Fariha Syaqina Zulkepli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Haze
010504 meteorology & atmospheric sciences Computer science Geography Planning and Development TJ807-830 010501 environmental sciences Management Monitoring Policy and Law Disease cluster Topology TD194-195 01 natural sciences Renewable energy sources topological data analysis GE1-350 Cluster analysis 0105 earth and related environmental sciences time delay embedding Persistent homology Environmental effects of industries and plants Renewable Energy Sustainability and the Environment persistent homology Hierarchical clustering haze Data set Environmental sciences Topological data analysis cluster analysis |
| Zdroj: | Sustainability Volume 12 Issue 10 Sustainability, Vol 12, Iss 3985, p 3985 (2020) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su12103985 |
| Popis: | Severe haze episodes have periodically occurred in Southeast Asia, specifically taunting Malaysia with adverse effects. A technique called cluster analysis was used to analyze these occurrences. Traditional cluster analysis, in particular, hierarchical agglomerative cluster analysis (HACA), was applied directly to data sets. The data sets may contain hidden patterns that can be explored. In this paper, this underlying information was captured via persistent homology, a topological data analysis (TDA) tool, which extracts topological features including components, holes, and cavities in the data sets. In particular, an improved version of HACA was proposed by combining HACA and persistent homology. Additionally, a comparative study between traditional HACA and improved HACA was done using particulate matter data, which was the major pollutant found during haze episodes by the Klang, Petaling Jaya, and Shah Alam air quality monitoring stations. The effectiveness of these two clustering approaches was evaluated based on their ability to cluster the months according to the haze condition. The results showed that clustering based on topological features via the improved HACA approach was able to correctly group the months with severe haze compared to clustering them without such features, and these results were consistent for all three locations. |
| Databáze: | OpenAIRE |
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