Projecting Australia's forest cover dynamics and exploring influential factors using deep learning

Long, Yue; Gao, Lei; Marcos-Martínez, Raymundo; Mallants, Dirk; Bryan, Brett A.

doi:10.1016/j.envsoft.2019.07.013

Cited by 51 publications

(16 citation statements)

References 68 publications

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“…Remotely sensed data and statistical methods have been widely used to project forest cover changes and investigate the drivers (e.g., Giriraj et al 2008;Hu et al 2014;Makinano-Santillan et al 2001;Nahib and Suryanta 2017). Machine learning (ML) methods have also been used for this purpose, which have great advantages in dealing with high-dimensional nonlinear spatial data and providing more accurate prediction and mapping results (Cracknell and Reading 2014;Micheletti et al 2014;Ye et al 2019). ML methods can be divided into two types-parametric and nonparametric-depending on whether they summarise data with a fixed number of parameters with respect to the sample size (Russell and Norvig 2016).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric machine learning for mapping forest cover and exploring influential factors

Liu

Gao

et al. 2020

Landscape Ecol

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The contribution of forest ecosystem services to human well-being varies over space following the dynamics in forest cover. Use of machine learning models is increasing in projecting forest cover changes and investigating the drivers, yet references are still lacking for selecting machine learning models for spatial projection of forest cover patterns. ObjectivesWe assessed the ability of nonparametric machine learning techniques to project the spatial distribution of forest cover and identify its drivers using a case study of Tasmania, Australia. MethodsWe developed, evaluated, and compared the performance of four nonparametric machine learning models: support vector regression (SVR), artificial neural networks (ANN), random forest (RF), and gradient boosted regression trees (GBRT). ResultsThe results demonstrated that RF far outperformed the other three models in both fitting and projection accuracy, and required less computional costs. GBRT outperformed SVR and ANN in projection accuracy. However, RF exhibited serious overfitting due to the full growth of its decision trees. The influence rankings of explanatory variables on spatial patterns of forest cover were different under the four models. Land tenure type and rainfall were identified among the top four most influential variables by all four models. The ranking produced by the RF model was significantly different with topographic factors associated with land clearing and production costs (elevation and distance to timber facilities) being the two most influential variables. ConclusionsWe encourage practitioners to consider nonparametric machine learning methods, especially RF, when facing problems of complex environmental data modelling.

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Section: Introductionmentioning

confidence: 99%

Nonparametric machine learning for mapping forest cover and exploring influential factors

Liu

Gao

et al. 2020

Landscape Ecol

Self Cite

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“…Vol.12 No.6 (2021), 2650-2665 Pseudo R-squared Logistic regression is different from R-squared in OLS recurrence. There are many types of pseudo R-squared measurements (Ye et al 2019). This metric does not mean the R-squared method in OLS regression.…”

Section: Research Articlementioning

confidence: 99%

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

Aggrawal¹

2021

TURCOMAT

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Background: Early speculation of cardiovascular disease can help determine the lifestyle change options of high-risk patients, thereby reducing difficulties. We propose a coronary heart disease data set analysis technique to predict people’s risk of danger based on people’s clinically determined history. The methods introduced may be integrated into multiple uses, such for developing decision support system, developing a risk management network, and help for experts and clinical staff. Methods: We employed the Framingham Heart study dataset, which is publicly available Kaggle, to train several machine learning classifiers such as logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), random forest (RF) and gradient boosting classifier (GBC) for disease prediction. The p-value method has been used for feature elimination, and the selected features have been incorporated for further prediction. Various thresholds are used with different classifiers to make predictions. In order to estimating the precision of the classifiers, ROC curve, confusion matrix and AUC value are considered for model verification. The performance of the six classifiers is used for comparison to predict chronic heart disease (CHD). Results: After applying the p-value backward elimination statistical method on the 10-year CHD data set, 6 significant features were selected from 14 features with p <0.5. In the performance of machine learning classifiers, GBC has the highest accuracy score, which is 87.61%. Conclusions: Statistical methods, such as the combination of p-value backward elimination method and machine learning classifiers, thereby improving the accuracy of the classifier and shortening the running time of the machine.

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“…Model system-level dynamics, interactions and feedback loops Predictive algorithms fail to capture system-level dynamics, scale-level dependence of human-forest interactions, social-ecological interactions, and feedback loops inherent in forest systems Thompson et al, 2012;Varshney, 2016;Selbst et al, 2019;Gonzalez et al, 2016;Kroll et al, 2016;Hofman et al, 2017Struss, 2004Ashraf et al, 2015;Norouzzadeh et al, 2018;Debeljak et al, 2001;Ye et al, 2019;Rao et al, 2020 Include social actors, institutions and broader context in decision-support systems Algorithms often miss or simplify complex social-ecological contexts and diverse set of social actors and institutions found in forestry contexts Rodrigues and de la Riva, 2014;Dutta et al, 2016;Hofman et al, 2017;Holloway and Mengersen, 2018;Mueller et al, 2019;Selbst et al, 2019;Salganik et al, 2020 Model synergies and tradeoffs, surprises and unintended consequences, non-linear relationships, time lags Failure of predictive algorithms to model domain characteristics of forest systems including dynamic and non-linear growths, thresholds, surprises, time lags, unintended characteristics and prevalence of synergies and tradeoffs among multiple objectives Thompson et al, 2012;Varshney, 2016;Hofman et al, 2017;Selbst et al, 2019 Understand human perceptions, behavior and attitudes Failure of predictive algorithms to model human behavior, perceptions and attitudes Dutta et al, 2016;Hofman et al, 2017: Nguyen et al, 2016Fang et al, 2017 System factors and outcome complexity due to regional and ecological variations Predictive algorithms fail to model inherent complexity and variability in forest system factors and outcomes due to regional and ecological variations Curtis et al, 2018;Franklin and Ahmed, 2018;…”

Section: System Complexitymentioning

confidence: 99%

“…Quantifying uncertainty is also difficult. For example, in using deep learning to project Australia's forest cover dynamics, it was difficult to make uncertainty projections due to the large number of model parameters (Ye et al, 2019). Transfer of models trained with particular sets of conditions in a given forest system to a new system with different kinds of conditions is difficult (Hart et al, 2019).…”

Section: Fairness and Justicementioning

confidence: 99%

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Rana

Varshney

2021

Front. For. Glob. Change

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Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations.

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Projecting Australia's forest cover dynamics and exploring influential factors using deep learning

Cited by 51 publications

References 68 publications

Nonparametric machine learning for mapping forest cover and exploring influential factors

Nonparametric machine learning for mapping forest cover and exploring influential factors

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

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