Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

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 platfor...

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Autores principales:	Hadi Farhadi, Mohammad Najafzadeh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Remote Sensing Google Earth Engine Random Forest Flood Risk Mapping Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/0d00f2a867d643ef88b8d41b2a2994e9
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spelling	oai:doaj.org-article:0d00f2a867d643ef88b8d41b2a2994e92021-11-11T19:57:45ZFlood Risk Mapping by Remote Sensing Data and Random Forest Technique10.3390/w132131152073-4441https://doaj.org/article/0d00f2a867d643ef88b8d41b2a2994e92021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3115https://doaj.org/toc/2073-4441Detecting 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.Hadi FarhadiMohammad NajafzadehMDPI AGarticleRemote SensingGoogle Earth EngineRandom ForestFlood Risk MappingHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3115, p 3115 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Remote Sensing Google Earth Engine Random Forest Flood Risk Mapping Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	Remote Sensing Google Earth Engine Random Forest Flood Risk Mapping Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Hadi Farhadi Mohammad Najafzadeh Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
description	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.
format	article
author	Hadi Farhadi Mohammad Najafzadeh
author_facet	Hadi Farhadi Mohammad Najafzadeh
author_sort	Hadi Farhadi
title	Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
title_short	Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
title_full	Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
title_fullStr	Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
title_full_unstemmed	Flood Risk Mapping by Remote Sensing Data and Random Forest Technique
title_sort	flood risk mapping by remote sensing data and random forest technique
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0d00f2a867d643ef88b8d41b2a2994e9
work_keys_str_mv	AT hadifarhadi floodriskmappingbyremotesensingdataandrandomforesttechnique AT mohammadnajafzadeh floodriskmappingbyremotesensingdataandrandomforesttechnique
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Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

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