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
Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling
Mohamed Saber,Tayeb Boulmaiz,Mawloud Guermoui,Karim I. Abdrabo,Sameh A. Kantoush,Tetsuya Sumi,Hamouda Boutaghane,Tomoharu Hori,Doan Van Binh,Binh Quang Nguyen,Ngoc Duong Vo,Emad Habib,Emad Mabrouk +12 more
TL;DR: In this article , three machine learning techniques, namely random forest (RF), LightGBM, and CatBoost, are compared with those of the rainfall-runoff model, and different training dataset sizes are utilized in the performance assessment.
Journal ArticleDOI
Climate Change and Its Impact on Brown Bear Distribution in Iran
Sahar Roshan Ara,Sohrab Ashrafi,Roghayeh Garmaeepour,Mohammad Zarrintab,Nariman Askaripour,Sorour Esfandeh +5 more
TL;DR: In this article , the effects of future climate changes on the distribution of brown bears using an ensemble modeling method in R-software were investigated, and five algorithms including MAXENT, RF, MARS, GAM, GLM and BRT were used to predict the distribution in the present climatic conditions as well as in the 2050s and 2070s.
Journal ArticleDOI
Prediction of flood routing results in the Central Anatolian region of Türkiye with various machine learning models
Journal ArticleDOI
Flood Susceptibility Mapping Using Image-Based 2D-CNN Deep Learning: Overview and Case Study Application Using Multiparametric Spatial Data in Data-Scarce Urban Environments
Yashon O. Ouma,Lawrence Omai +1 more
TL;DR: In this article , the authors presented results for urban flood susceptibility mapping (FSM) using image-based 2D-convolutional neural networks (2D-CNN) using multiparametric spatial data comprised of land-useland-cover (LULC), digital elevation model (DEM), and the topographic and hydrologic conditioning derivatives, precipitation, and soil types.
Journal ArticleDOI
Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models
TL;DR: In this article , the authors used four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB), to predict current and future flood susceptibility under three climate change scenarios.
References
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