A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains cha...

Full description

Saved in:
Bibliographic Details
Published in:Journal of environmental management 2021-02, Vol.280, p.111858-111858, Article 111858
Main Authors: Ngo, Phuong-Thao Thi, Pham, Tien Dat, Hoang, Nhat-Duc, Tran, Dang An, Amiri, Mahdis, Le, Thu Trang, Hoa, Pham Viet, Bui, Phong Van, Nhu, Viet-Ha, Bui, Dieu Tien
Format: Article
Language:English
Subjects:
Cyclonic Storms
Data Mining
Decision tree
Equilibrium optimizer
Flash flood
Floods
GIS
Rivers
SysFor
Tropical storm
Vietnam
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts. •HE-SysFor is proposed for flash flood modeling.•Ten flash flood indicators were considered.•Elevation, slope, land cover, and rainfall are the most important indicators.•HE-SysFor has a high performance, better than benchmarks, SVM-RBF, LReg, C4.5, and DeepLNN.•HE-SysFor and wrapper technique are new tools for flash flood study.
ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2020.111858