We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.

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soft computing benchmark models including sgd logistic regression lr logistic model tree lmt and functional tree ft algorithms hybrid functional machine learning algorithm adaboost ab meta classifier namely absgd model ensemble model root mean square error absgd model auc 0860 ft 0734 noise landslide conditioning factors sensitivity functional algorithm stochastic gradient descent sgd power prediction predict the spatial distribution of landslides in leastsquare support vector machine lssvm area under the receiver operatic characteristic auc lr 0797 sgd 0776 lmt sarkhoon watershed

,.Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. 11 (8),.

,"Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm" 11

,"Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm"