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
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
Regional-scale prediction of pluvial and flash flood susceptible areas using tree-based classifiers
TL;DR: In this paper , a novel methodology based on tree-based classifiers that considers both spatially distributed and catchment-related influencing factors was developed to enable regional-scale susceptibility assessment, which can be used to prioritize cities for detailed investigations.
Journal ArticleDOI
The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning
TL;DR: Wang et al. as mentioned in this paper constructed a conceptual method to investigate the spatial variation of urban flood susceptibility based on the machine learning models (i.e., Convolution Neural Network (CNN) and Support Vector Machine (SVM)), which has been tested in Wuhan City of China with good performances.
Journal ArticleDOI
Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China
TL;DR: Wang et al. as discussed by the authors proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River.
Book ChapterDOI
Application of Artificial Intelligence in Predicting Groundwater Contaminants
TL;DR: In this article, the authors present a critical review of the application of artificial intelligence (AI) in developing prediction models of globally concerning groundwater contaminants, including arsenic, fluoride, and nitrate.
Journal ArticleDOI
Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region
Quoc Bao Pham,Subodh Chandra Pal,Asish Saha,Indrajit Chowdhuri,Jasem A Albanai,Saeid Janizadeh,Koursoh Ahmadi,Khaled Mohamed Khedher,Duong Tran Anh,Weili Duan +9 more
References
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