Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects.
Land use changes (LUCs), which are defined as the modification in the use of land due to anthropogenic activities, are important sources of GHG emissions. In this context, understanding future trends of LUCs, such as deforestation, in a spatial manner is relevant. The main objective of this study is...
| Published in: | Journal of Industrial Ecology Vol. 26; no. 1; pp. 225 - 240 |
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| Main Authors: | , |
| Format: | Article |
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Springer Nature
Feb2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155254435&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155254435 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: N pubinfo: dt: Feb2022 vid: 26 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 155254435 10.1111/jiec.13185 ppf: 225 ppct: 15 formats: tig: atl: Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects. aug: au: Larrea‐Gallegos, Gustavo Vázquez‐Rowe, Ian affil: Peruvian LCA and Industrial Ecology Network (PELCAN), Department of Engineering, Pontificia Universidad Católica del Perú, Lima, , Peru su: Peru Deforestation Road construction Machine learning Random forest algorithms Construction projects sug: subj: Deforestation Peru Highway, Street, and Bridge Construction Road construction Machine learning Random forest algorithms Construction projects keyword: Amazon rainforest climate change industrial ecology life cycle assessment random forest Amazon rainforest climate change industrial ecology life cycle assessment random forest ab: Land use changes (LUCs), which are defined as the modification in the use of land due to anthropogenic activities, are important sources of GHG emissions. In this context, understanding future trends of LUCs, such as deforestation, in a spatial manner is relevant. The main objective of this study is to generate a deforestation prediction model for a given period of time (i.e., 2002–2017 and 2010–2017) to estimate the potential carbon emissions associated with different anthropogenic variables in the Peruvian Amazon using machine learning (ML) algorithms. This study was motivated in the analysis of a road project previously studied using life cycle assessment (LCA). Models using neural networks and random forest algorithms were trained and evaluated in a fully cloud‐based environment using Google Earth Engine. ML‐related results demonstrated that random forest is a quicker and straightforward response to model the system under study, especially considering that data do not require additional processing during the modeling and prediction stages. Predicted results suggest that expected road expansion may be related to considerable carbon emissions in the future. Calculated values are relevant especially if the mitigation efforts that Peru has complied with in the Paris Agreement are considered. The increased complexity of the framework is justified since it allows identifying the location of hotspots and may potentially complement the utility of LCA in policy support in the areas of territorial planning and tropical road expansion. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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