Soil, a non-renewable resource, sustains life on Earth by supporting around 95% of global
food production and providing ecosystem services such as biomass production, filtration of …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Floods are natural hazards that lead to devastating financial losses and large displacements
of people. Flood susceptibility maps can improve mitigation measures according to the …

Flash floods are among the world most destructive natural disasters, and develo**
optimum hybrid Machine Learning (ML) models for flash flood susceptibility (FFS) modeling …

The combined effects of climate change and accelerated urbanization have increased the
risk of urban rainstorm and flood disasters, which seriously affects the achievement of the …

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the
Indian Himalaya cause considerable damage, posing a serious risk to life and property. This …

Rapid prediction of urban flood is essential for sustainable city and society development.
The data-driven deep learning model is commonly adopted for flood prediction, but it rarely …

This review provides a synthesis of advances in our understanding of urban flood processes
and their modelling over the last four years (2018–2021). Four aspects are covered …

Abstract Decision Support System (DSS) is an approach for smart management of different
man-made and natural phenomena such as flood disasters. In the present study, different …

Urban flooding research cannot be overemphasized due to its environmental, economic and
societal concern. Research on the response of urban flooding under different rainstorm …