Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects

Gustavo Larrea‐Gallegos and Ian Vázquez‐Rowe

Journal of Industrial Ecology, 2022, vol. 26, issue 1, 225-239

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jiec.13185

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:inecol:v:26:y:2022:i:1:p:225-239

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1088-1980

Access Statistics for this article

Journal of Industrial Ecology is currently edited by Reid Lifset

More articles in Journal of Industrial Ecology from Yale University
Bibliographic data for series maintained by Wiley Content Delivery ().