Data analytics approach to create waste generation profiles for waste management and collection

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Data analytics approach to create waste generation profiles for waste management and collection. / Niska, Harri; Serkkola, Ari.

In: Waste Management, Vol. 77, 07.2018, p. 477-485.

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@article{d0852fae686a444bb04e6d6448600ca4,
title = "Data analytics approach to create waste generation profiles for waste management and collection",
abstract = "Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling.",
keywords = "Cluster analysis, Data analytics, Data mining, Machine learning, Waste generation, Waste monitoring",
author = "Harri Niska and Ari Serkkola",
year = "2018",
month = "7",
doi = "10.1016/j.wasman.2018.04.033",
language = "English",
volume = "77",
pages = "477--485",
journal = "Waste Management",
issn = "0956-053X",

}

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TY - JOUR

T1 - Data analytics approach to create waste generation profiles for waste management and collection

AU - Niska, Harri

AU - Serkkola, Ari

PY - 2018/7

Y1 - 2018/7

N2 - Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling.

AB - Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling.

KW - Cluster analysis

KW - Data analytics

KW - Data mining

KW - Machine learning

KW - Waste generation

KW - Waste monitoring

UR - http://www.scopus.com/inward/record.url?scp=85046371348&partnerID=8YFLogxK

U2 - 10.1016/j.wasman.2018.04.033

DO - 10.1016/j.wasman.2018.04.033

M3 - Review Article

VL - 77

SP - 477

EP - 485

JO - Waste Management

JF - Waste Management

SN - 0956-053X

ER -

ID: 16799391