RF, XGBoost and NGBoost Machine Learning Approaches for Landslide Mapping: Insights from Trabzon Province, TurkeyFebruary 6, 2025February 6, 2025
Stacked Machine Learning: A Game Changer for Ground Anchor Capacity EstimationFebruary 6, 2025February 6, 2025
Vietnam Researchers Develop 12 Models to Predict CBR of Stabilized Soil with Industrial WasteFebruary 2, 2025February 6, 2025
Artificial Neural Network Approach for Predicting Pile Settlement Using Standard Penetration Test Data: A Case Study from IranJanuary 29, 2025February 6, 2025
Optimizing Land Use Mapping Using Conventional and Metaheuristic Algorithms: Insights from Cairns, Queensland, AustraliaJanuary 27, 2025February 6, 2025
Landslide Susceptibility in Sikkim’s Rangit River Basin: Ensemble Models to the RescueJanuary 21, 2025February 6, 2025
ANN-Based Landslide Susceptibility Mapping for Koyulhisar: A Study from TurkeyJanuary 19, 2025February 6, 2025
PSO-Optimized GB, RF, SVM, and KNN Hybrid Model Developed in Vietnam for Shallow Foundation AnalysisJanuary 17, 2025February 6, 2025
Hybrid ANN Framework with ICA, GA, ABC, and PSO for Dynamic and Static Slope Stability: A Study Conducted in IranJanuary 15, 2025February 6, 2025
VIT Vellore Researcher Advances Ultimate Bearing Capacity Prediction with Relevance Vector MachineJanuary 14, 2025February 6, 2025
Neural Network Model Inspired by Emotional Intelligence for Liquefaction Susceptibility: A Study in Patna, IndiaJanuary 13, 2025February 6, 2025
A researcher from Jordan developed SVM, QDA, and DT machine learning models for predicting soil liquefactionJanuary 12, 2025February 6, 2025
