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 …

The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …

Flash flood risk assessment, a widely applied technology in preventing catastrophic flash
flood disasters, has become the current research hotspot. However, most existing machine …

In recent years, there has been a growing interest in flood susceptibility modeling. In this
study, we conducted a bibliometric analysis followed by a meta-data analysis to capture the …

With the development of remote sensing algorithms and increased access to satellite data,
generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly …

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

Predicting crop yield is an important issue for farmers. Food security is important for decision-
makers. The agriculture industry can more accurately supply human demand for food if the …

Flood susceptibility maps are useful tool for planners and emergency management
professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 …

This study aims to examine three machine learning (ML) techniques, namely random forest
(RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu …

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a
robust deep-learning (DP) model based on a combination of extreme learning machine …