Abstract
Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.
Highlights
Flash floods are those events where the rise in water is rapid within a few hours of the heavy rainfall
Construction of buildings and roads in urban areas resulted in the increase of surface areas of impermeable structures and less infiltration and more runoff, causing flash floods in the event of intense rainfall during short periods [40,126,127]
The results of flood approaches to predict the possibility of flash flood occurrences in a studied catchment of Iran, where susceptibility zoning in Tafresh watershed showed that the southeastern parts have high to very high devastating flash flood events are frequent
Summary
Flash floods are those events where the rise in water is rapid within a few hours of the heavy rainfall. Flash flood is one of the most common, severely devastating natural hazards, which causes significant damages to the infrastructure and socioeconomy, and most importantly, it brings loss of lives [1,2,3,4,5]. The most destructive nature of flood events is generally related to the extreme amount of torrential rainfall within a short duration resulting in high surface runoff [4,7]. Flash floods occur within catchments, where the response time of the drainage basin is short. According to the American Meteorological Society, flash flood events generally do not give advance warning and they cause significant risk and destruction due to their complex and dynamic environmental settings and nature [8,9]
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