Optimizing the
accuracy of flood extent mapping using multitemporal stack of Sentinel-1 SAR data
with machine learning approach for Wardha River, Chandrapur District (India)
Pusdekar Prashant N. and Dudul Sanjay V.
Disaster Advances; Vol. 16(12); 12-19;
doi: https://doi.org/10.25303/1612da012019; (2023)
Abstract
Floods are the most common, destructive and frequently occurring natural disasters
on the earth in terms of economic damages and affected lives. A flood can be an
inconvenience or a catastrophic event, resulting in long-term economic and environmental
consequences. Flood extent mapping identifies and delineates the areas that are
inundated. The study focuses on the flood event of Wardha river near Chandrapur
on 12th August, 2022. In this study, we proposed an ensemble averaging model (EAM)
for optimizing the accuracy of flood inundation mapping that discriminates flood
waters from the non-flood waters using stack of multitemporal Sentinel-1 satellite
imagery. Sentinel-1 uses C-band microwave signals to measure backscatter from the
Earth's surface with its synthetic aperture radar (SAR) sensor that can penetrate
clouds and collects data regardless of weather conditions.
The results of the proposed model were compared with other machine learning models
such as SVM, RF and MLC. The result analysis reveals that the overall accuracy,
Kappa coefficient (KC) and area under curve (AUC) values for the proposed model
(OA = 98%, KC = 0.97, AUC = 0.986 for training and OA = 97%, KC = 0.96, AUC = 0.957
for testing dataset) outperformed the other models. The result may help people and
town planners in identifying safe and risky areas in the study area.
