# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Natural Hazards  
Natural hazards such as droughts, hurricanes, tornadoes, floods, and landslides can affect human life and property [101]. Accurate prediction or mapping the probabilities of natural disasters is therefore of great importance for human survival. Machine learning algorithms, particularly ensemble algorithms, have high prediction accuracy [85] and so have become increasingly used in identifying areas of long term drought [63], mapping landslide hazards [81; 90], assessing the susceptibility of gullies to erosion [102; 103] and mapping land susceptible to subsidence [78].  
[PERSON] et al. [62] combined three meta classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) into a hybrid ensemble framework to predict the likelihood of flooding in the Gorganroud River Basin, Iran. [PERSON] et al. [65] used multiple ensemble algorithms (GBM, RF, parallel random forest (PRF), regularized random forest (RRF), and ERT) to quantify the likelihood of flash flooding in Markazi Province, Iran and found that ERT was the most accurate model with an area under curve (AUC) value of 0.82. [PERSON] et al. [68] combined bagging with logistic model trees (LMT) and developed a bagging-LMT ensemble model to map flood susceptibility. The results showed that in terms of accurately mapping flood susceptibility, the bagging-LMT model performed better than LMT, logistic regression, Bayesian logistic regression, and RF. [PERSON] et al. [78] compared logistic regression, MLP and two meta ensemble machine learning algorithms (AdaBoost and LogitBoost) in predicting likely subsidence based on a land subsidence inventory map generated from Sentinel-1 synthetic aperture radar (SAR) data. The results showed that AdaBoost gave the greatest prediction accuracy (81.1%), followed by MLP (80%), logistic regression (79.4%), and LogitBoost (79.1%). [PERSON] et al. [81] investigated the suitability of flexible discriminant analysis (FDA), generalized logistic models (GLM), GBM, and RF for mapping landslide susceptibility. The test results showed that FDA was similar in prediction accuracy to GLM but was less accurate than GBM, which was in turn less accurate than RF. [PERSON] et al. [87] compared a Bayesian regularization back propagation neural network (BRBP), CART, an evidence belief function (EBF) and their various combinations in ensemble models to predict flood likelihood in Bangladesh. They found that the ensemble model that combined BRBP, CART, and EBF using weighted averaging was more accurate (AUC > 90%) than other models. In another study, [PERSON] et al. [86] found that stacking locally weighted linear regression (LWLR) and RF models increased the prediction accuracy of flood susceptibility maps in Bangladesh, with R\({}^{2}\) = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148. [PERSON] et al. [90] used a majority voting ensemble technique to predict landslide susceptibility and found that the ensemble model that combined logistic regression, GBDT, and voting feature intervals produced predictions that were close in accuracy to the predictions of widely used machine learning algorithms such as decision trees, SVM, and RF.