Journal Article10.1016/J.ENVSOFT.2017.06.012
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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TL;DR: 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.
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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.
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Citations
Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran
Sina Paryani,Mojgan Bordbar,Changhyun Jun,Mahdi Panahi,S. M. Bateni,Christopher M. U. Neale,Hamid Reza Moeini,Saro Lee +7 more
TL;DR: This study aims at optimizing the support vector regression (SVR) model using four metaheuristic methods, Harris hawks optimization (HHO), particle swarm optimization (PSO), gray wolf optimizer (GWO), and bat algorithm to create a reliable flood susceptibility map (FSM).
Flood susceptibility mapping to improve models of species distributions
Elham Ebrahimi,Miguel B. Araújo,Babak Naimi +2 more
On the application of machine learning into flood modeling: data consideration and modeling algorithm
Ali Pourzangbar,Peter Oberle,Andreas Kron,Mario J. Franca +3 more
TL;DR: This article reviews the literature on the application of Machine Learning (ML) to identify flood-prone areas, covering studies published since 2013, and finds ensemble and hybrid models generally outperform traditional ML methods, despite their own limitations.
Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques
Jatan Debnath,Dhrubojyoti Sahariah,Nityaranjan Nath,Anup Saikia,Durlov Lahon,Md. Nazrul Islam,Shizuka Hashimoto,Gowhar Meraj,Pankaj Kumar,Suraj Kumar Singh,Shruti Kanga,Kesar Chand +11 more
TL;DR: This study integrates multicollinearity tests and geospatial techniques to model flood risk susceptibility in the Jia Bharali River basin, Eastern Himalayas, identifying moderate to very high flood-prone zones and recommending FL and AHP models for application.
Assessing Flood Hazard at River Basin Scale with an Index-Based Approach: The Case of Mouriki, Greece
Olga Patrikaki,Nerantzis Kazakis,Ioannis Kougias,Thomas Patsialis,Nicolaos Theodossiou,Konstantinos Voudouris +5 more
- 03 Feb 2018
TL;DR: In this paper, the authors applied a multicriteria index method to assess flood hazard areas at a river basin scale, in a geographic information system (GIS) environment, by processing information of seven parameters: flow accumulation, distance from the drainage network, elevation, land use, rainfall intensity and geology.
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
76.1K
Bagging predictors
Leo Breiman
- 01 Aug 1996
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
- 01 Jan 1979
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.
6.7K
A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant
Keith Beven,Mike Kirkby +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that combines the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple luminescence.
5.7K