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Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data.

Dong Luo¹, Marcellus M Caldas¹, Douglas G Goodin¹

¹Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, 66502, USA.

Journal of Environmental Management

|April 11, 2021

Summary

This study used machine learning and Twitter data to map environmental vulnerability in the Brazilian Cerrado. High vulnerability areas increased, particularly in the South, correlating with social media activity.

Keywords:

Autoencoder Cerrado Environmental vulnerability Machine learning Twitter data

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Area of Science:

Environmental science
Machine learning
Social media analytics

Background:

Estimating environmental vulnerability is crucial for understanding human impacts.
The Brazilian Cerrado faces significant environmental pressures.

Purpose of the Study:

To integrate machine learning and Twitter data for environmental vulnerability estimation in the Brazilian Cerrado.
To assess changes in environmental vulnerability between 2011 and 2016.

Main Methods:

Developed an environmental vulnerability model using exposure and sensitivity indicators.
Applied an Autoencoder algorithm for data representation and the Displaced Ideal method for vulnerability estimation.
Validated results using historical Twitter data from 2011 and 2016.

Main Results:

High environmental vulnerability areas increased from 58.44% in 2011 to 62.87% in 2016, concentrated in the Southern Cerrado.
Over 85% of analyzed tweets originated from high environmental vulnerability zones.
The Autoencoder algorithm proved effective for environmental assessment.

Conclusions:

Machine learning and social media data offer a novel approach for regional environmental vulnerability assessment.
Social media data can effectively analyze the human-environment relationship.
Future research should focus on developing more comprehensive indicator systems to enhance model performance.