Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Abstract

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

Flash Flood Assessment and Management for Sustainable Development Using Geospatial Technology and WMS Models in Abha City, Aseer Region, Saudi Arabia

“…The accuracy of the flood risk map is affected by the accuracy and quality of the data used. The more accurate the map produced, the higher the spatial resolution of the data [82]. RS technologies select the best spectral bands for detecting changes in the natural environment [83].…”

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

“…In Equation ( 2) refers only to the flooding areas which are detected by satellite. Therefore coastal lakes and other water bodies, as well as, vegetate and dense urbanized areas were removed ( [69][70][71]). After model calibration, a number of simulations were carried out, forced by the same rainfall of the event 25 November 2018, with different values of the pumping rates in Mazzocchio station and of the sea level rise, with the goal to identify the most vulnerable areas to flooding and the type and location of the flood defence constructions, as levees, dikes or flood expansion areas.…”

Optimal flood susceptibility model based on performance comparisons of LR, EGB, and RF algorithms

“…In addition, the methods of GIS help to create a spatial database, which is considered as the cornerstone for ood susceptibility analysis and delineating of ood-prone zones (Zhao et al, 2019). Various methods have been developed to map ood-susceptible areas, including the "analytical hierarchy process (AHP) (Seejata et al, 2018;Das and Gupta, 2021); frequency ratio (FR) and index of entropy (IoE) (Feizizadeh et al, 2021), logistic regression (LR)" (Band et al 2020;Malik et al 2020), the extreme gradient boosting (EGB) (Naghibi et al, 2020;Mirzaei et al, 2021), and random forest (RF) (Farhadi and Najafzadeh, 2021).…”