Floods, as major natural disasters, cause massive property destruction and death.
Understanding the occurrence time of this event by advance notice helps consider …

Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are
popular deep learning architectures currently used for rapid flood simulations. However …

The preservation of cultural heritage is confronted with significant challenges due to its
extensive history and the increasing impact of climate change, particularly natural disasters …

This study addresses the notable absence of comprehensive syntheses and bibliometric
analyses concerning the utilization of spatial technology within urban heritage preservation …

The extensive expansion of impervious surfaces encroaches on green spaces and causes
frequent urban waterlogging disasters. Previous studies have focused mainly on the …

This study proposes a multidimensional framework for the assessment of cultural heritage
vulnerability to flood hazards. In this framework, cultural heritage is framed within a …

Lessons learned from previous flood disasters are significant in mitigating future flood
damages. Therefore, this study aims at understanding the disastrous damage caused by the …

This article investigates long-term adaptation and resilience to floods at World Heritage sites
through a case study of Hoi An Ancient Town, Quang Nam, Vietnam. Drawing on fieldwork …

This study investigates the impact of digital economy on carbon emissions and energy
transition, focusing on G-20 countries and Chinese cities from 2008 to 2021. Using data from …

High spatial heterogeneity of urban floods poses challenges in its modeling and
assessment, and a flood database is a basic requirement but it is lacking for many cities. The …