Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana

Abstract

Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km 2 area of the watershed is flooded every 2-3 years (normal flooding) and about 689.61 km 2 is flooded every 7-10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

“…If there are trees in the RF, the sum of the GI changes of feature in all decision trees is [ 36 , 37 ]: …”

Flood inundation assessment in the data-scarce region using hydrodynamic models and google earth engine -A case of Ghed region, Ozat River basin, Gujarat, India

“…Floodplain zoning, damage analysis, and proper inundation mapping are likely to be possible with models integrated with the image analysis of the Landsat satellite (Thakkar et al 2017). One positive thing is that Landsat images are freely available; most researchers can only use this (Ghansah et al 2021;Mehmood et al 2021;Sivanpillai et al 2021). This land cover and damage analysis of land classes are likely to be the most successful technique for flood modelers (Nandi et al 2017).…”