Application of high-dimensional uniform manifold approximation and projection (UMAP) to cluster existing landfills on the basis of geographical and environmental features

Teng-To Yu; Chun-Yuan Chen; Tai-Hsi Wu; Yu-Chen Chang

doi:10.1016/j.scitotenv.2023.167013

Application of high-dimensional uniform manifold approximation and projection (UMAP) to cluster existing landfills on the basis of geographical and environmental features

Sci Total Environ. 2023 Dec 15:904:167013. doi: 10.1016/j.scitotenv.2023.167013. Epub 2023 Sep 11.

Authors

Teng-To Yu¹, Chun-Yuan Chen², Tai-Hsi Wu³, Yu-Chen Chang⁴

Affiliations

¹ Department of Resources Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC. Electronic address: yutt@mail.ncku.edu.tw.
² Department of Resources Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC; Ministry of Environment, No. 83, Zhonghua Rd. Sec. 1, Zhongzheng District, Taipei City 100006, Taiwan, ROC. Electronic address: chuyuachen@moenv.gov.tw.
³ Department of Business Administration, National Taipei University, No. 151, University Rd., Sanxia Dist., New Taipei City 237303, Taiwan, ROC. Electronic address: taiwu@mail.ntpu.edu.tw.
⁴ Ministry of Environment, No. 83, Zhonghua Rd. Sec. 1, Zhongzheng District, Taipei City 100006, Taiwan, ROC. Electronic address: yuchen.chang@moenv.gov.tw.

PMID: 37704152
DOI: 10.1016/j.scitotenv.2023.167013

Abstract

Due to extreme conditions, which are influenced by the location of landfills, the release of pollutants has been recently proven to be more severe in estuary landfills, as these landfill locations are affected by both sea-water and river-water interactions. To identify geographic and environmental features linked to the extreme conditions of certain landfills, a high-dimensional clustering method combining Uniform Manifold Approximation and Projection (UMAP) with the Louvain algorithm is proposed. A case study was conducted using 17 noteworthy features that transform to Landfill Suitability Index (LSI) applied to hundreds of landfill sites in Taiwan. This study clustered landfills into 10 clusters and identified several clusters with significant extreme locations, including estuary landfills (7.9 %), fault-water-body landfills (8.2 %), and densely-populated-water-body landfills (17.6 %). Furthermore, a critical discovery of endangered Platalea minor habitats near these estuary landfills was made. Additionally, this work identified "healthy" landfills (11.2 %) that are minimally affected by the considered features. These findings demonstrate the promising potential of our framework for managers to systematically improve landfill management strategies. Moreover, our framework was tested by incorporating rainfall and flooding features in relation to climate change scenarios. To address the demand for land release from occupied landfills in Taiwan, there is a pressing need to expedite the transition to a circular economy, and our framework can provide further assistance in this regard. This approach is promising, as it provides a new method to evaluate the environmental risks linked to landfills and also identifies potential opportunities related to landfill mining. Finally, this work was extended to include a case study in England, which has 19,801 landfills and a dataset containing 15 relevant landfill features; in this case study, our framework identified 110 landfill clusters, and several placed in extreme locations, demonstrating that our framework is flexible for use in other regions outside of Taiwan.

Keywords: Landfill; Landfill Suitability Index (LSI); Louvain algorithm; Machine learning; Platalea minor; Uniform Manifold Approximation and Projection (UMAP).