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 …

Coasts worldwide are facing enormous challenges relating to extreme water levels,
inundation and coastal erosion. These challenges need to be addressed with consideration …

Integrating powerful machine learning models with flood risk assessment and determining
the potential mechanism between risk and the driving factors are crucial for improving flood …

Climate change and increasing urbanization are two primary factors responsible for the
increased risk of serious flooding around the world. The prediction and monitoring of the …

In this study, the flood hazards susceptibility map of an area in Turkey which is frequently
exposed to flooding was predicted by training 70% of inventory data. For this, statistical, and …

The present study was done in order to simulate the flash-flood susceptibility across the
Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria …

The research aims to propose the new ensemble models by combining the machine
learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k …

Floods, one of the most common natural hazards globally, are challenging to anticipate and
estimate accurately. This study aims to demonstrate the predictive ability of four ensemble …

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have
been carried out in recent years. While majority of those models produce reasonably …

Floods persist as a recurring and daunting peril in the Brahmaputra plain of Assam.
Notwithstanding advancement, Bongaigaon is a highly flood-afflicted district in the lower part …