Journal ArticleDOI
A novel hybrid artificial intelligence approach for flood susceptibility assessment
Kamran Chapi,Vijay P. Singh,Ataollah Shirzadi,Himan Shahabi,Dieu Tien Bui,Binh Thai Pham,Khabat Khosravi +6 more
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TLDR
Results indicate that the proposed Bagging-LMT model can be used for sustainable management of flood-prone areas and outperformed all state-of-the-art benchmark soft computing models.Abstract:
A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas.read more
Citations
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Journal ArticleDOI
Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution
Ataollah Shirzadi,Karim Solaimani,Mahmood Habibnejad Roshan,Ataollah Kavian,Kamran Chapi,Himan Shahabi,Saskia Keesstra,Baharin Bin Ahmad,Dieu Tien Bui +8 more
TL;DR: In this paper, the effect of different sample sizes and raster resolutions in landslide susceptibility modeling and prediction accuracy of shallow landslides was evaluated in the Bijar region of the Kurdistan province (Iran) was selected as a case study.
Journal ArticleDOI
Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models.
TL;DR: The main aim of the present study is represented by the calculation of Flash-Flood Potential Index within the upper and the middle sector of Prahova river catchment (Romania) by using 4 hybrid models: Logistic Regression-Frequency Ratio (LR-FR) model, Logisticregression-Weights of Evidence (LR)-WoE model, Support Vector Machine-F Frequency Ratio (SVM-FR).
Journal ArticleDOI
Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania.
TL;DR: The results show that the prediction capability of the proposed ensemble models varied from 86.8% (the RF-FR model) to 93.9% ( the RF-WOE model), which indicate a high prediction performance for all the models.
Journal ArticleDOI
Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models
TL;DR: In this article, the authors compared results of flood susceptibility modelling in the part of Middle Ganga Plain, Ganga foreland basin, and found that 12 major flood explanatory factors were included.
Journal ArticleDOI
Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management
TL;DR: One of the novelties of this research is related to the application of Fuzzy Support Vector Machine ensemble for the first time in a study concerning the evaluation of the susceptibility to a certain natural hazard.
References
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Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
A physically based, variable contributing area model of basin hydrology
Mike Kirkby,Keith Beven +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models.
Journal ArticleDOI
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
TL;DR: The use of ranks to avoid the assumption of normality implicit in the analysis of variance has been studied in this article, where the use of rank to avoid normality is discussed.