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Scholars Journal of Engineering and Technology | Volume-13 | Issue-07
Urban Flood Modeling and Mitigation Strategies Using Remote Sensing and GIS
Zhang Ling, Yu Jing, Muhammad Aqeel, Fatima Siddiqa, Liang Yan, Mao Wenlong
Published: July 22, 2025 | 61 49
Pages: 535-551
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Abstract
The climate change, haphazard urbanization, and poor drainage system have increased urban flooding especially in cities that are rapidly growing and the current flood models fail to capture high-resolution spatial information and interactive inclusion of socio-environmental elements, especially in the underdeveloped areas such as South Asia. This paper has helped fill this gap by developing a flood risk assessment framework of Lahore, Pakistan basing on Remote Sensing (SAR) and GIS-based measurement of rainfall interactions with urban density, drainage capacity, and topography interactions. The ultimate goals were to (1) establish the flood prone areas with utmost accuracy, (2) extract the main flood cause drivers, and (3) present the corresponding mitigation fractions that could be constructed upon (sustainable and data-driven). Land use was classified in Sentinel-2 and Landsat data, and the extent of flood delineation was done using Sentinel-1 SAR imagery (20152023). Topography was evaluated using Digital Elevation Models (SRTM, ALOS PALSAR) and rainfall intensity was provided by meteorological records. Predictors of flooding were assessed by statistical analyses (logistic/linear regression, Random Forest). It was found out that flooded areas were characterized by a considerably greater urban density (63.4% vs. 31.6%, *p* < 0.001), a decreased level of drainage (2.24 vs. 4.26 km/km2, *p* < 0.001), and smaller elevation (203.9 vs. 218.4 m, *p* < 0.001). Logistic regression model worked with precision of 85 percent (AUC = 0.96) and reflected rain intensity and urban density as major risk factors (OR = 1.10, *p* = 0.001 and OR = 1.065, *p* = 0.009 respectively). This study shows that a combination of SAR and GIS will help in modeling flooding in the data-deficient areas to offer useful information to urban planners. The main implication is the fact that drainage improvements should be given priority in dense, low-based regions and green areas maintained to reduce run