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Article

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

1
Department of Photogrammetry and Remote Sensing, Faculty of Surveying Engineering, K. N. Toosi University of Technology, Tehran 19697-64499, Iran
2
Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman 7631885356, Iran
*
Author to whom correspondence should be addressed.
Academic Editors: Zheng Duan, Babak Mohammadi and Yongqiang Zhang
Water 2021, 13(21), 3115; https://doi.org/10.3390/w13213115
Received: 31 August 2021 / Revised: 31 October 2021 / Accepted: 1 November 2021 / Published: 4 November 2021
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. View Full-Text
Keywords: Remote Sensing; Google Earth Engine; Random Forest; Flood Risk Mapping Remote Sensing; Google Earth Engine; Random Forest; Flood Risk Mapping
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MDPI and ACS Style

Farhadi, H.; Najafzadeh, M. Flood Risk Mapping by Remote Sensing Data and Random Forest Technique. Water 2021, 13, 3115. https://doi.org/10.3390/w13213115

AMA Style

Farhadi H, Najafzadeh M. Flood Risk Mapping by Remote Sensing Data and Random Forest Technique. Water. 2021; 13(21):3115. https://doi.org/10.3390/w13213115

Chicago/Turabian Style

Farhadi, Hadi, and Mohammad Najafzadeh. 2021. "Flood Risk Mapping by Remote Sensing Data and Random Forest Technique" Water 13, no. 21: 3115. https://doi.org/10.3390/w13213115

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