مشخصات پژوهش

عنوان	A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Landslide, machine learning, Bayes-based theory, meta-classifiers, Iran
چکیده	A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC = 0.930). However, RS model (AUROC = 0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC = 0.972), MB (AUROC = 0.970) and AB (AUROC = 0.957) models, respectively.
پژوهشگران	دیو تین بویی (نفر ششم به بعد)، بهارین بن احمد (نفر ششم به بعد)، بین تایی فام (نفر ششم به بعد)، کامران چپی (نفر پنجم)، هیمن شهابی (نفر چهارم)، عطااله شیرزادی (نفر سوم)، بهاره قاسمیان (نفر دوم)، موسی عابدینی (نفر اول)