… , we also developed the ensemble machine learning algorithms like alternating … flood
susceptibility modelling in the Teesta river basin. However, Dagging ensemble machine learning …

… susceptibility modeling. The effective feature selection method (IGR) … for flood susceptibility
modeling. It was determined that the most effective conditioning factor for flood susceptibility …

… machine learning and statistical models are implemented and compared, and based on
their results, seven ensemble models for flood susceptibility … The data set covered 201 flood …

… To prepare maps of flood susceptible areas and predict flood risk probabilities for RCPs 2.6,
4.5, 6.0 and 8.5 in association with other flood influencing factors based on the suitable ML …

… Hence, in this work, the quantile method was opted for generating flood susceptibility classes.
Flood susceptible maps of the study area were classified into five zones, namely very low, …

… Floods are one of the most widespread natural hazards occurring across the globe. The main
objective of this study was to produce flood susceptibility … ) and two machine learning (ML) …

… Flood susceptibility projections relying on standalone models… This study proposed novel
integrative flood susceptibility … algorithms, integrated with machine learning models: generalized …

… and magnitude of future flood events. The purpose of this research was to estimate flash
flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, ie, …

… in machine learning areas in recent years. It is chosen for this study to assess the flood
susceptibility … is compared with three widely used supervised machine learning models: LR, ANN, …

… The key contributions of this research are: (i) to generate relevant models for the determination
of flood susceptible areas; and (ii) produce new flood hazard map for Bangladesh, using …