Abstract
Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.
Highlights
Floods are among the most destructive natural disasters [1]
The extremely randomized trees (ERT) model (AUC = 0.82) was most optimal according to its predictive capacity, considering the values of area under curve (AUC) and different statistical indices, though there was a slight variation among the predicted models and its associated AUC values
From the perspective of bias-variance, a justification for the extra trees model was that the implied randomization of the cut-point and assign mixed with the ensemble average had to be able to minimize the deviation quite positively than lesser randomization strategies considered by different techniques
Summary
Floods are among the most destructive natural disasters [1]. The term flash flood can be defined as a phenomenon in which river water flows from its natural levees and causes inundation of the surrounding areas for a specific time [2]. Over the last few decades, ongoing global climate change has been associated with an increase in the frequency and magnitude of global flash flood hazards. This is not the only reason why large-scale human intervention in the environment, such as forest ecosystems, e.g., deforestation, sedimentation in riverbeds, and riverbeds’ encroachment by human settlements and dam construction along with unhealthy development of urbanization, are responsible for devastating flash floods. It has been estimated that 31% of total global economic losses with $104 billion are caused by flood hazards, known as the most costly natural disaster, among others [10]. The Kalvan watershed has been affected by flooding annually; studies show that the Kalvan watershed is vulnerable to flash floods
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