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
More filters
Journal ArticleDOI
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
Wei Chen,Mahdi Panahi,Paraskevas Tsangaratos,Himan Shahabi,Ioanna Ilia,Somayeh Panahi,Shaojun Li,Abolfazl Jaafari,Baharin Bin Ahmad +8 more
TL;DR: The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility.
Journal ArticleDOI
River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.
TL;DR: The results showed that the CART model performed best in predicting SSL, and can be a helpful tool in basins where hydro-meteorological data are readily available.
Journal ArticleDOI
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Dieu Tien Bui,Phuong Thao Thi Ngo,Tien Dat Pham,Abolfazl Jaafari,Nguyen Quang Minh,Pham Viet Hoa,Pijush Samui +6 more
TL;DR: A new soft computing approach that is an integration of an Extreme Learning Machine and a Particle Swarm Optimization, named as PSO-ELM, for the spatial prediction of flash flood susceptibility at high frequency tropical typhoon areas is proposed and validated.
Journal ArticleDOI
Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)
Mohammad Ahmadlou,Mohammad Karimi,S. Alizadeh,Ataollah Shirzadi,D. Parvinnejhad,Himan Shahabi,Mahdi Panahi +6 more
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran shows its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.
Journal ArticleDOI
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi,Ataollah Shirzadi,Kayvan Ghaderi,Ebrahim Omidvar,Nadhir Al-Ansari,John J. Clague,Marten Geertsema,Khabat Khosravi,Ata Amini,Sepideh Bahrami,Omid Rahmati,Kyoumars Habibi,Ayub Mohammadi,Hoang Nguyen,Assefa M. Melesse,Baharin Bin Ahmad,Anuar Ahmad +16 more
TL;DR: The results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models and should be more widely applied for the sustainable management of flood-prone areas.
References
More filters
Journal ArticleDOI
Random Forests
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Bagging predictors
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
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.
Journal ArticleDOI
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
TL;DR: The use of ranks to avoid the assumption of normality implicit in the analysis of variance has been studied in this article, where the use of rank to avoid normality is discussed.