A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha,n = 33), White-naped Crane (Grus vipio,n = 40), and Black-necked Crane (Grus nigricollis,n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

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A Predictive Model of Rectal Tumor Response to Preoperative Radiotherapy Using Classification and Regression Tree Methods

Clinical Cancer Research ◽

10.1158/1078-0432.ccr-04-2587 ◽

2005 ◽

Vol 11 (15) ◽

pp. 5440-5443 ◽

Cited By ~ 36

Author(s):

Inti Zlobec ◽

Russell Steele ◽

Nilima Nigam ◽

Carolyn C. Compton

Keyword(s):

Predictive Model ◽

Tumor Response ◽

Preoperative Radiotherapy ◽

Regression Tree ◽

Rectal Tumor ◽

Classification And Regression Tree ◽

Tree Methods ◽

Classification And Regression

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Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/3/14952 ◽

2020 ◽

Vol 42 (3) ◽

Cited By ~ 4

Author(s):

Bahareh Ghasemain ◽

Dawod Talebpoor Asl ◽

Binh Thai Pham ◽

Mohammadtghi Avand ◽

Huu Duy Nguyen ◽

...

Keyword(s):

Landslide Susceptibility ◽

Regression Tree ◽

Shallow Landslide ◽

Susceptibility Mapping ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Landslide Susceptibility Mapping ◽

Positive Rate ◽

Classification And Regression ◽

Cart Model

Shallow landslides through land degrading not only lead to threat the properly and life of human but they also may produce huge ecosystem damages. The aim of this study was to compare the performance of two decision tree machine learning algorithms including classification and regression tree (CART) and reduced error pruning tree (REPTree) for shallow landslide susceptibility mapping in Bijar, Kurdistan province, Iran. We first used 20 conditioning factors and then they were tested by information gain ratio (IGR) technique to select the most important ones. We then constructed a geodatabase based on the selected factors along with a total of 111 landslide locations with a ratio of 80/20 (for calibration/validation). The performance of the models was checked by the true positive rate (TP Rate), false positive rate (FP Rate), precision, recall, F1-Measure, Kappa, mean absolute error, and area under the receiver operatic curve (AUC). Results of IGR specified that the slope angle and TWI had the most contribution to shallow landslide occurrence in the study area. Moreover, results concluded that although these models had a high goodness-of-fit and prediction accuracy, the CART model (AUC=0.856) outperformed the REPTree model (AUC=0.837). Therefore, the CART model can be used as a promising tool and also as a base classifier to hybrid with optimization algorithms and Meta classifiers for spatial prediction of shallow landslide-prone areas.

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