... in volumetric water content and crest settlement. Results revealed critical thresholds between 85 and 90 mm/h for the test material, beyond which infiltration accelerated and deformation intensified. Seven regression-based machine learning models, namely, support vector regression, K-nearest neighbours, random...

...Ba-Quang-Vinh Nguyen; Viet-Long Doan Conditioning factors (CFs), such as topographic, hydrological, and environmental factors, significantly influence the accuracy of predicting landslide spatial probability ( LSP ). This study applied three machine learning models – random forest ( RF ), deep...

...Dong Li, MSc; Zhenlong Jiang, MSc; Kuo Tian, PhD; Ran Ji, PhD Six machine learning methods (linear regression, logistic regression, extreme gradient boosting (XGBoost), support vector machine, K-nearest neighbours and artificial neural network) were used to predict/classify the hydraulic...