Use of freely available datasets and machine learning methods in predicting deforestation. (January 2017)

• Record Type: Journal Article
• Authors: Mayfield, Helen; Smith, Carl; Gallagher, Marcus; Hockings, Marc
• Is Part Of: Environmental modelling & software. Volume 87(2017)
• Page Start: 17
• Page End: 28
• Publication Date: 2017-01
• Subjects: Artificial neural network -- Bayesian network -- Deforestation -- Freely available data -- Gaussian process -- Logistic regression; ANN Artificial neural networks -- BN Bayesian networks -- CI Conservation International -- DEM Digital elevation model -- FN False negative -- FP False positive -- GLM Generalised linear model -- GLMM Generalised linear mixed model -- GP Gaussian process -- IUCN International Union for the Conservation of Nature -- ML Machine learning -- NE Natural Earth -- PA Protected area -- TAN Tree Augmented Naïve -- TN True negative -- TP True positive -- TSS True skill statistic -- AUC area under the (receiver operating) curve -- WDPA World database on protected areas -- WWF World Wildlife Fund; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.10.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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.