In this paper, we proposed a novel approach for flood risk assessment, which is a
combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The …

Improving the accuracy of flood prediction and mapping is crucial for reducing damage
resulting from flood events. In this study, we proposed and validated three ensemble models …

In mountainous regions, landslides and flash floods are common and dangerous natural
hazards. They often happen at the same time and seriously affect residential areas and …

In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi
district of India through the developmentof different novel GIS based soft computing …

In this paper, we developed highly accurate ensemble machine learning models integrating
Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D) …

Landslide susceptibility map is considered as one of the important steps in assessing
vulnerability of an area to landslide hazard. In this study, the main objective is to propose …

Water supply is a key challenge and priority for achieving sustainable development goals in
many countries. Recognizing areas with groundwater potential is crucial in addressing this …

In the class imbalanced learning scenario, traditional machine learning algorithms focusing
on optimizing the overall accuracy tend to achieve poor classification performance …

AdaBoost has been theoretically and empirically proved to be a very successful ensemble
learning algorithm, which iteratively generates a set of diverse weak learners and combines …

This study propose a new approach through which the landslide susceptibility in Quang
Nam (Vietnam) will be estimated using the best model among the following algorithms …