Detecting Areas Vulnerable to Flooding Using Hydrological-Topographic Factors and Logistic Regression

Abstract

As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models

“…When the slope angle is less, the probability of flooding increases (Rahman et al 2019 ). Runoff from rainfall accumulates and inundates areas with gentle slopes due to the low flow velocity in these areas (Lee and Kim 2021 ). The slope of the district ranges from 0 to 74.48° (Fig.…”

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

“…Non-deterministic methods, including both statistical and machine learning approaches, are useful for developing predictive solutions in classification. Statistical methods like the analytical hierarchy process (AHP) [10], logistic regression (LR) [11], and frequency ratio (FR) [12], as well as machine learning methods like support vector machines (SVMs) [13], random forest (RFs) [14], and convolutional neural networks (CNNs) [15], are widely utilized for this purpose.…”

Topographic map registration method based on logistic regression

“…Given an input, it can output a floating point number between [0,1], which is the probability value of an event, so as to achieve the effect of classification. It has been widely used in many fields such as disaster prediction [3], satellite cloud image detection [4], disease identification [5], text classification [6] and data mining. In view of the fact that this method has not been applied to image registration, this paper studies the method of topographic map registration using Logistic regression, and trains the prediction model with a batch of topographic maps as the initial data, and uses the model to carry out topographic map registration.…”