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Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwestcoast of India

The flood hazards in the southwest coastal region of India in 2018 and 2020 resulted in numerous casualties and the displacement of over a million people from their homes. In order to mitigate the loss of life and resources caused by recurrent major and minor flood events, it is imperative to develop a comprehensive spatial flood zonation map of the entire area. Therefore, the main aim of the present study is to prepare a flood susceptible map of the southwest coastal region of India using synthetic-aperture radar (SAR) data and robust machine learning algorithms. Accurate flood and non-flood locations have been identified from the multi-temporal Sentinel-1 images. These flood locations are correlated with sixteen flood conditioning geo-environmental variables. The Boruta algorithm has been applied to determine the importance of each flood conditioning parameter. Six efficient machine learning models, namely support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), partial least squares (PLS) and penalized discriminant analysis (PDA) have been applied to delineate the flood susceptible areas of the study region. The performance of the models has been evaluated using several statistical criteria, including area under curve (AUC), overall accuracy, specificity, sensitivity and kappa index. The results have revealed that all models have performed more than 90% of AUC due to the high precision of radar data. However, the RF and SVM models have outperformed other models in terms of all statistical parameters. The findings have identified approximately 13% of the study region as highly vulnerable to flood hazards, emphasizing the need for proper planning and management in these areas.

Ketersediaan
220551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
13 hlm PDF, 8.291 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.24, December 2024
Subjek
Machine Learning
GIS
Flood disaster
Southwest coastal region of India
SAR
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwestcoast of India
    The flood hazards in the southwest coastal region of India in 2018 and 2020 resulted in numerous casualties and the displacement of over a million people from their homes. In order to mitigate the loss of life and resources caused by recurrent major and minor flood events, it is imperative to develop a comprehensive spatial flood zonation map of the entire area. Therefore, the main aim of the present study is to prepare a flood susceptible map of the southwest coastal region of India using synthetic-aperture radar (SAR) data and robust machine learning algorithms. Accurate flood and non-flood locations have been identified from the multi-temporal Sentinel-1 images. These flood locations are correlated with sixteen flood conditioning geo-environmental variables. The Boruta algorithm has been applied to determine the importance of each flood conditioning parameter. Six efficient machine learning models, namely support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), partial least squares (PLS) and penalized discriminant analysis (PDA) have been applied to delineate the flood susceptible areas of the study region. The performance of the models has been evaluated using several statistical criteria, including area under curve (AUC), overall accuracy, specificity, sensitivity and kappa index. The results have revealed that all models have performed more than 90% of AUC due to the high precision of radar data. However, the RF and SVM models have outperformed other models in terms of all statistical parameters. The findings have identified approximately 13% of the study region as highly vulnerable to flood hazards, emphasizing the need for proper planning and management in these areas.
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