Pham Viet Hoa

TL;DR: It could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.

TL;DR: A new soft computing approach that is an integration of an Extreme Learning Machine and a Particle Swarm Optimization, named as PSO-ELM, for the spatial prediction of flash flood susceptibility at high frequency tropical typhoon areas is proposed and validated.

TL;DR: It is concluded that the Keras’s deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas and is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree.

TL;DR: It is concluded that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.

TL;DR: The proposed hybrid PSO-MARS model outperformed other benchmark models in all the employed statistical measures and can be particularly suited for flash flood forecasting problems at high frequency tropical typhoon area.