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
Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.
Ataollah Shirzadi,Karim Soliamani,Mahmood Habibnejhad,Ataollah Kavian,Kamran Chapi,Himan Shahabi,Wei Chen,Khabat Khosravi,Binh Thai Pham,Biswajeet Pradhan,Biswajeet Pradhan,Anuar Ahmad,Baharin Bin Ahmad,Dieu Tien Bui +13 more
TL;DR: The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
Journal ArticleDOI
Integrated machine learning methods with resampling algorithms for flood susceptibility prediction.
Esmaeel Dodangeh,Bahram Choubin,Ahmad Najafi Eigdir,Narjes Nabipour,Mehdi Panahi,Shahaboddin Shamshirband,Amir Mosavi,Amir Mosavi +7 more
TL;DR: Novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling and bootstrapping algorithms, integrated with machine learning models outperformed the benchmark models such as Standalone GAM, MARS, BRT, multilayer perceptron (MLP) and support vector machine (SVM).
Journal ArticleDOI
Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis
Mahfuzur Rahman,Mahfuzur Rahman,Chen Ningsheng,Monirul Islam,Ashraf Dewan,Javed Iqbal,Javed Iqbal,Rana Muhammad Ali Washakh,Tian Shufeng +8 more
TL;DR: This study offers a new opportunity to the relevant authority for planning and designing flood control measures by integrating statistical, machine learning, and multi-criteria decision analysis, including artificial neural network (ANN), logistic regression (LR), frequency ratio (FR), and analytical hierarchy process.
Journal ArticleDOI
A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.
Khabat Khosravi,Majid Sartaj,Frank T.-C. Tsai,Vijay P. Singh,Nerantzis Kazakis,Assefa M. Melesse,Indra Prakash,Dieu Tien Bui,Binh Thai Pham +8 more
TL;DR: The results show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91 and the most effective contributing factor for ground water vulnerability in the study area is the net recharge.
Journal ArticleDOI
Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India
TL;DR: In this article, the effectiveness of EBF, binomial logistic regression (LR) and ensemble EBF and LR (EBF-LR) model with remote sensing and GIS techniques for flood susceptibility mapping and spatial prediction of flood-susceptible areas in the Koiya river basin of West Bengal, India.
References
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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.
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