Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods

Dieu Tien Bui; Paraskevas Tsangaratos; Phuong-Thao Thi Ngo; Tien Dat Pham; Binh Thai Pham

doi:10.1016/j.scitotenv.2019.02.422

Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods

Sci Total Environ. 2019 Jun 10:668:1038-1054. doi: 10.1016/j.scitotenv.2019.02.422. Epub 2019 Mar 6.

Authors

Dieu Tien Bui¹, Paraskevas Tsangaratos², Phuong-Thao Thi Ngo³, Tien Dat Pham⁴, Binh Thai Pham⁵

Affiliations

¹ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam. Electronic address: dieu.t.bui@usn.no.
² Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: paraskevas.tsangaratos@tdtu.edu.vn.
³ Department of Geoinformatics, Faculty of Information Technology, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi, Viet Nam. Electronic address: ngothiphuongthao@humg.edu.vn.
⁴ Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan. Electronic address: tiendat.pham@riken.jp.
⁵ Geotechnical Engineering and Artificial Intelligence Research Group (GEOAI), University of Transport Technology, Hano, Viet Nam. Electronic address: binhpt@utt.edu.vn.

PMID: 31018446
DOI: 10.1016/j.scitotenv.2019.02.422

Abstract

The main objective of the present study was to provide a novel methodological approach for flash flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative flash-flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for flash flood susceptibility mapping.

Keywords: Bagging and boosting models; FURIA; Flash flood susceptibility; Genetic algorithms; Vietnam.