Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.
By Farzaneh Sajedi Hosseini, Bahram Choubin, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Hamid Darabi, A. Torabi Haghighi. Science of the Total Environment, 2019
304 citations recorded by the source corpus.
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. 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.
Subfields: Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/d34b0401e93d491485eff5c0d2429aec25e6d8e4
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Citations in the corpus
- Selecting critical features for data classification based on machine learning methods (2020, background): “…[31], local climate zone [32], environmental noise pollution [33], urban pollution [34, 35], rainfall spatial temporal [36], flash flood hazard [37, 38], landslide [39], earthquake damage detection using curvilinear features [40], earthquake classifiers using stochastic reconstruction [41] and…”