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
An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping
Omid Rahmati,Davoud Davoudi Moghaddam,Vahid Moosavi,Zahra Kalantari,Mahmood Samadi,Saro Lee,Dieu Tien Bui +6 more
TL;DR: A simple random sampling method is applied to determine the best method to estimate groundwater potential mapping accuracy in the presence of non-volatilevolatile groundwater conditions.
Journal ArticleDOI
An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review
Hafiz Suliman Munawar,Ahmed W. A. Hammad,S. Travis Waller,Muhammad Jamaluddin Thaheem,Asheem Shrestha +4 more
TL;DR: A novel framework has been proposed to optimise flood management with the application of a holistic approach and what are the existing gaps in the selected technologies for post-disaster scenario?
Journal ArticleDOI
Improving urban flood susceptibility mapping using transfer learning
TL;DR: This study investigated the utility of transfer learning to improve urban flood susceptibility assessment using knowledge outside the training domain and found that TL typically provided positive effects when it transferred knowledge from a well pretrained model in data-rich catchment, but may induce negative effects when the pre-trained model has poor accuracy.
Journal ArticleDOI
Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model
TL;DR: Considering the slope units as their reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)].
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A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain-oriented survey
TL;DR: The main conclusion of the research work is that the integration of the derived knowledge from an application domain-oriented survey into the general benchmarking framework along with the set of guidelines for best or proper CI algorithms selection can improve significantly the forecasting accuracy and the response time, in case of real time forecasters.
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.