Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

• Published in: Forests Vol. 12; no. 4; p. 395
• Main Authors: Koreň, Milan, Jakuš, Rastislav, Zápotocký, Martin, Barka, Ivan, Holuša, Jaroslav, Ďuračiová, Renata, Blaženec, Miroslav
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2021
• Subjects: Age; Algorithms; Bark; bark beetle; Bayesian analysis; Beetles; classifier; Classifiers; Coniferous forests; Damage assessment; Damage prevention; Decision trees; Discriminant analysis; forest damage; Forest management; Geographic information systems; geographical information system; Infestation; Learning algorithms; Machine learning; Military training; Neural networks; Normalized difference vegetative index; Pest outbreaks; Precipitation; Radiation; Solar radiation; Spatial distribution; spatial distribution model; Support vector machines; Time series; Central Europe; Czech Republic
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f12040395

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.