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
Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool
TL;DR: In this paper , a Long Short-Term Memory (LSTM) network was used to predict soil moisture and streamflow over multiple watersheds using spatially and temporally varying hydrological and meteorological data.
Journal ArticleDOI
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
Ahmed M. Al-Areeq,Sani Isah Abba,Mohamed A. Yassin,Mohammed Benaafi,Mustafa Ghaleb,Isam H. Aljundi +5 more
TL;DR: In this paper , the authors used four ensemble algorithms for assessing flood risk in Jeddah City, Saudi Arabia: bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbor (KNN).
Journal ArticleDOI
Performance Evaluation of Hospital Site Suitability Using Multilayer Perceptron (MLP) and Analytical Hierarchy Process (AHP) Models in Malacca, Malaysia
TL;DR: In this paper , the authors used correlation-based feature selection (CFS) with a search algorithm (greedy stepwise) to identify the most significant conditioning parameters that impact the choice of an appropriate hospital site.
Journal ArticleDOI
Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression
Soroosh Mehravar,Seyed Vahid Razavi-Termeh,Armin Moghimi,Babak Ranjgar,Fatemeh Foroughnia,Meisam Amani +5 more
TL;DR: In this article , a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR), which was applied to the hybridized SVR models to map flood susceptibility in Ahwaz township, Iran.
Journal ArticleDOI
Hybrid approach for flood susceptibility assessment in a flood-prone mountainous catchment in China
TL;DR: Wang et al. as mentioned in this paper developed a hybrid approach that integrates hydrodynamic model (HEC-HMS/RAS), rapid flood model (height above the nearest drainage, HAND) and machine learning model (random forest, RF) for flood susceptibility assessment.
References
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