Spatial Flood Forecasting Modeling under Lack of Data Using RS and Optimized Support Vector Machine: A Case Study of the Zahedan Watershed

Mirkazemi, Seyyed Meisam; Jalalkamali, Navid; Irandoost, Mohsen

doi:10.1061/nhrefo.nheng-1665

Cited by 1 publication

(1 citation statement)

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“…In the field of flood mapping, numerous research studies have used machine learning algorithms to effectively predict and map flood-prone areas. Some commonly used ML algorithms include the random forest (RF) [23][24][25], Support Vector Machine (SVM) [26,27], Gaussian Process Regression (GPR) [28], Decision Tree (DT) [29], and Artificial Neural Network (ANN) [30]. In the present study, we employed the random forest algorithm to identify the flood-prone areas of Kashkan River in the Zagros Mountain range, located in the west of Iran.…”

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confidence: 99%

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

Ghahraman,

Nagy,

Nooshin Nokhandan

2023

Geosciences

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We utilized the random forest (RF) machine learning algorithm, along with nine topographical/morphological factors, namely aspect, slope, geomorphons, plan curvature, profile curvature, terrain roughness index, surface texture, topographic wetness index (TWI), and elevation. Our objective was to identify flood-prone areas along the meandering Kashkan River and investigate the role of topography in riverbank inundation. To validate the flood susceptibility map generated by the random forest algorithm, we employed Sentinel-1 GRDH SAR imagery from the March 2019 flooding event in the Kashkan river. The SNAP software and the OTSU thresholding method were utilized to extract the flooded/inundated areas from the SAR imagery. The results showed that the random forest model accurately pinpointed areas with a “very high” and “high” risk of flooding. Through analysis of the cross-sections and SAR-based flood maps, we discovered that the topographical confinement of the meander played a crucial role in the extent of inundation along the meandering path. Moreover, the findings indicated that the inner banks along the Kashkan river were more prone to flooding compared to the outer banks.

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