@article{ref1,
title="Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method",
journal="Science of the total environment",
year="2019",
author="Hosseini, Farzaneh Sajedi and Choubin, Bahram and Mosavi, Amir and Nabipour, Narjes and Shamshirband, Shahaboddin and Darabi, Hamid and Haghighi, Ali Torabi",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. <br><br>RESULTS of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90-92%, Kappa = 79-84%, Success ratio = 94-96%, Threat score = 80-84%, and Heidke skill score = 79-84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.<br><br>Copyright © 2019 Elsevier B.V. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0048-9697",
doi="10.1016/j.scitotenv.2019.135161",
url="http://dx.doi.org/10.1016/j.scitotenv.2019.135161"
}