Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Clicks: 267
ID: 261194
2019
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Abstract
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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bui2019remoteshallow Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Himan Shahabi;Ebrahim Omidvar;Ataollah Shirzadi;Marten Geertsema;John J. Clague;Khabat Khosravi;Biswajeet Pradhan;Binh Thai Pham;Kamran Chapi;Zahra Barati;Baharin Bin Ahmad;Hosein Rahmani;Gyula Gróf;Saro Lee;Tien Bui, Dieu;Shahabi, Himan;Omidvar, Ebrahim;Shirzadi, Ataollah;Geertsema, Marten;Clague, John J.;Khosravi, Khabat;Pradhan, Biswajeet;Pham, Binh Thai;Chapi, Kamran;Barati, Zahra;Bin Ahmad, Baharin;Rahmani, Hosein;Gróf, Gyula;Lee, Saro;
Journal remote sensing
Year 2019
DOI
10.3390/rs11080931
URL
https://dx.doi.org/10.3390/rs11080931
Keywords
Iran gis adaboost landslide modeling stochastic gradient descent meta classifier

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