scholarly journals Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Landslides ◽  
2015 ◽  
Vol 13 (5) ◽  
pp. 1315-1318 ◽  
Author(s):  
Ahmed Mohamed Youssef ◽  
Hamid Reza Pourghasemi ◽  
Zohre Sadat Pourtaghi ◽  
Mohamed M. Al-Katheeri
Keyword(s):  
Saudi Arabia ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Linear Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Landslide Susceptibility Mapping ◽  
Boosted Regression Tree ◽  
Classification And Regression ◽  
Asir Region

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

2020 ◽  
Vol 42 (3) ◽  
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.

scholarly journals Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

2019 ◽  
Vol 9 (5) ◽  
pp. 942 ◽  
Author(s):  
Soyoung Park ◽  
Jinsoo Kim
Keyword(s):  
Random Forest ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Information Gain ◽  
Regression Tree ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Boosted Regression Tree ◽  
Prediction Rate ◽  
Better Than

This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning.

scholarly journals Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2849 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Yumin Guo ◽  
Xuesong Han ◽  
Lijia Wen
Keyword(s):  
Random Forest ◽  
Species Distribution ◽  
Performance Metrics ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Supporting Evidence ◽  
Boosted Regression Tree ◽  
Stochastic Gradient Boosting

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.

scholarly journals Why to choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

2016 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Yumin Guo ◽  
Xuesong Han ◽  
Lijia Wen
Keyword(s):  
Random Forest ◽  
Species Distribution ◽  
Performance Metrics ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Supporting Evidence ◽  
Boosted Regression Tree ◽  
Stochastic Gradient Boosting

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) Besides, we developed an ensemble forecast by averaging predicted probability of above four models results. Commonly-used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. 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 by now, 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 it allows robust and rapid assessments and decisions for efficient conservation.

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