Testing the accuracy of machine learning methods to predict deforestation

Flores Caceres, Ivan Andres

Permalink

https://hdl.handle.net/2142/109618 Copy

Description

Title

Testing the accuracy of machine learning methods to predict deforestation

Author(s)

Flores Caceres, Ivan Andres

Issue Date

2020-12-07

Director of Research (if dissertation) or Advisor (if thesis)

Baylis, Kathy

Committee Member(s)

• Michelson, Hope
• Christensen, Peter

Department of Study

Agr & Consumer Economics

Discipline

Agricultural & Applied Econ

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2021-03-05T21:45:43Z

Keyword(s)

Machine Learning, Deforestation

Abstract

Forest plays a crucial role in meeting climate change goals, given its emissions reduction effects through carbon dioxide capture. The study of deforestation becomes significantly relevant since the early prediction of forest under threat could lead to specific policy responses promoting conservation measures. Common deforestation patterns are fish-bone, radial, geometric, and diffuse. This thesis aims to explore the predictive power of machine learning techniques to predict spatial patterns of human activities and compare their accuracy of prediction with a traditional statistical method. Using Monte Carlo simulations, land cover data was generated, mimicking human settlement patterns related to underlying deforestation processes. This work tests how different machine learning methodologies perform, after various experiments with diverse sources of data. The main result indicates that decision tree-based methodologies provide better prediction performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.

Graduation Semester

2020-12

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/109618

Copyright and License Information

Copyright 2020 Ivan Flores Caceres

Owning Collections