… and deep learning methods for flood risk assessment, but also … In summary, these MLMs
have been proven to have … It combines several weak base classifiers into a strong one and has …

… , K-nearest neighbor and Linear discriminant analysis have … this research paper the data
has been collected from Pakistan Meteorological Department and Machine Learning Classifiers …

… for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria
Decision Analysis (… of the flood risk assessment involves three main elements: hazard, …

… flood risk prediction as an early warning system is highly essential. This study aims to
develop a predictive … They conducted experiment using three Bayesian classifier algorithms …

… The resulting ranges of flood risk probabilities are predicted as 39… where is the prediction by
the tth weak classifier, is the … above analysis are further used to study what effect the various …

… The core idea is to train different weak classifiers for the same training set, and … flood risk
analysis, it is very important to choose the appropriate conditioning factors. This study analyzed …

… We developed a new machine learning technique for analysis … of the study were to: i) assess
the role of different factors … this study, urban flood hazard was quantitatively predicted using …

… flood prediction system using a combination of Machine Learning classifiers along with GIS
techniques to be used … the Hot Spot analysis were then combined to create a flood risk index. …

… a parametric classifier based on the hyperparameters used in … analysis of the results provided
by the following 6 machine … each flood predictor pixels into flood and non-flood categories …

… study introduced a novel flood risk assessment approach that integrates flood hazard mapping
using ML techniques and flood … In summary, the SVM model performed the best in terms …