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

• Published in: The Science of the total environment Vol. 904; p. 167013
• Main Authors: Yu, Teng-To, Chen, Chun-Yuan, Wu, Tai-Hsi, Chang, Yu-Chen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.12.2023
• Subjects: algorithms; case studies; circular economy; climate change; data collection; England; environment; estuaries; Landfill; Landfill Suitability Index (LSI); landfills; Louvain algorithm; Machine learning; Platalea minor; rain; river water; seawater; Taiwan; Uniform Manifold Approximation and Projection (UMAP); England; Taiwan; UMAP; LSI; RNA; Louvain algorithm; LCA; Uniform Manifold Approximation and Projection (UMAP); MSW; GIS; Platalea minor; Landfill; Machine learning; NGOs; MCDM; EIA; Landfill Suitability Index (LSI)
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2023.167013

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. [Display omitted] •Uniform Manifold Approximation and Projection (UMAP) clusters existing landfills•The UMAP-assisted Louvain clustering strategy is applied to analyze existing landfills.•UMAP is applied to leading cases embedded to evaluate the clustering results.•UMAP clustering helps to find more landfill sites worth mining and enhancing care.•The Estuary landfill located in Platalea minor habitats reveals high-risk co-effects.