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
Reads0
Chats0
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
Predicting wetland area and water depth in Barind plain of India
Pankaj Singha,Swades Pal +1 more
Journal ArticleDOI
Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms
Shavan Askar,Sajjad Zeraat Peyma,M. Yousef,Natalia Alekseevna Prodanova,Iskandar Muda,Mohamed Elsahabi,Javad Hatamiafkoueieh +6 more
TL;DR: In this paper , the authors developed new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), and harmony search (HS), with the decision table classifier (DTB).
Journal ArticleDOI
Dynamic Assessment of the Flood Risk at Basin Scale under Simulation of Land-Use Scenarios and Spatialization Technology of Factor
Jun Liu,Jiyan Wang,Junnan Xiong,Weiming Cheng,Xingjie Cui,Wen He,Yufeng He,Yu Duan,Gang Yang,Nan Wang +9 more
TL;DR: Wang et al. as discussed by the authors developed a new framework for a basin scale that employs a future land-use simulation model, a factor spatialization technique, and a novel hybrid model for scenario-based flood risk assessment in 2030 and 2050.
Journal ArticleDOI
Correction to: Using hybrid artificial intelligence approach based on a neuro‑fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran
Solmaz Abdollahizad,Mohammad Ali Balafar,Bakhtiar Feizizadeh,Amin Babazadeh Sangar,Karim Samadzamini +4 more
TL;DR: In this paper, an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA), was proposed.
Proceedings ArticleDOI
Computer-assisted Children Physical Fitness Detection and Exercise Intervention Evaluation based on Artificial Intelligence Model
Zhonghao Wu,Shenghua Qi +1 more
TL;DR: Computer-assisted children physical fitness detection and exercise intervention evaluation based on artificial intelligence model is implemented in this research and the experimental results have proven the effectiveness of the method.
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.