Use of freely available datasets and machine learning methods in predicting deforestation

• DOI: 10.1016/j.envsoft.2016.10.006
• Publication Date: 2016-10-29T01:30:16Z
• Authors: Helen Mayfield; Carl Smith; Marcus Gallagher; Marc Hockings
• Keywords: Artificial neural network; Logistic regression; 006; 2302 Ecological Modelling; 15. Life on land; 01 natural sciences; 1712 Software; Freely available data; Bayesian network; 2305 Environmental Engineering; 13. Climate action; Deforestation; Gaussian process; 0105 earth and related environmental sciences

The range and quality of freely available geo-referenced datasets is increasing. We evaluate the usefulness of free datasets for deforestation prediction by comparing generalised linear models and generalised linear mixed models (GLMMs) with a variety of machine learning models (Bayesian networks, artificial neural networks and Gaussian processes) across two study regions. Freely available datasets were able to generate plausible risk maps of deforestation using all techniques for study zones in both Mexico and Madagascar. Artificial neural networks outperformed GLMMs in the Madagascan (average AUC 0.83 vs 0.80), but not the Mexican study zone (average AUC 0.81 vs 0.89). In Mexico and Madagascar, Gaussian processes (average AUC 0.89, 0.85) and structured Bayesian networks (average AUC 0.88, 0.82) performed at least as well as GLMMs (average AUC 0.89, 0.80). Bayesian networks produced more stable results across different sampling methods. Gaussian processes performed well (average AUC 0.85) with fewer predictor variables. Freely available datasets have proven valuable in predicating deforestation.Machine learning techniques are a reliable alternative to statistics.Gaussian processes are suggested as an alternative to artificial neural networks.Bayesian networks were more stable across sample methods.