Floods are one of nature's most destructive disasters because of the immense damage to
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …

China suffers the most serious loss of life and property with the most floods in the world. In
this study, a multi-criteria analysis model with the combined analytic hierarchy process and …

This study is an attempt to quantitatively test and compare novel advanced-machine
learning algorithms in terms of their performance in achieving the goal of predicting flood …

The flooding in Bangladesh during monsoon season is very common and frequently
happens. Consequently, people have been experiencing tremendous damage to properties …

The main objective of this work is the future prediction of the floods in India due to climate
and land change. Human activity and related carbon emissions are the primary cause of …

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 …

This study presents two machine learning models, namely, the light gradient boosting
machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting …

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 are one of the most destructive natural disasters causing financial damages and
casualties every year worldwide. Recently, the combination of data‐driven techniques with …

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous
phenomena, particularly during the monsoon season. This type of flood occurs within a short …