Flood susceptibility assessment using extreme gradient boosting (EGB), Iran
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RESEARCH ARTICLE
Flood susceptibility assessment using extreme gradient boosting (EGB), Iran Sajjad Mirzaei 1 & Mehdi Vafakhah 1
&
Biswajeet Pradhan 2,3,4 & Seyed Jalil Alavi 1
Received: 1 June 2020 / Accepted: 24 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.
Communicated by: H. Babaie * Mehdi Vafakhah [email protected] Sajjad Mirzaei [email protected] Biswajeet Pradhan [email protected]; [email protected] Seyed Jalil Alavi [email protected] 1
Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Mazandaran 46417-76489, Iran
2
The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, New South Wales 2007, Australia
3
Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah 21589 80234, Saudi Arabia
4
Earth Observation Center Institute of Climate Change, Universiti Kebangsaan Malaysia, Selangor 43600 UKM Bangi,
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