Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Abstract

In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

“…Soil type has a direct impact on water storage, permeability and drainage (Mojaddadi et al 2017). Hence, this factor was also used in our analysis.…”

“…The ratio was highest (1562) when SPI was 690,985 to 1,151,642, followed by a ratio of 99 when SPI was between 0 and 230,328 (Table 1). Usually, areas with lower power streams are more susceptible to flooding (Mojaddadi et al 2017). This is due to the fact that most of the regions with higher SPI values are located on the slope of the mountains and steep areas, where flooding will not occur.…”

Predictive applications of Australian flood loss models after a temporal and spatial transfer

“…In recent decades, flood impacts have increased (Kreibich et al 2007;Cheng and Thompson 2016;McMillan et al 2016;Mojaddadi et al 2017), reaching 29% of the total cost of Australian natural disasters (Bureau of Transport Economics 2001). Hence, flood risk evaluation including hazard assessment and estimation of the associated consequences (Ciullo et al 2016;Vojtek and Vojtekov a 2016) has attracted growing attention (Raaijmakers et al 2008;Merz et al 2010;Cammerer et al 2013;Kundzewicz et al 2013).…”

Evaluating the Variations in the Flood Susceptibility Maps Accuracies Due to the Alterations in the Type and Extent of the Flood Inventory

“…The catchment is the mixture of urban, rural-forestry and grazing land. To build a flood susceptibility model two datasets of flood inventory and causative factors are required (Mojaddadi et al, 2017). Seven sets of flood inventory maps were prepared and used in the current flood susceptibility mapping.…”