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Flood susceptibility assessment using artificial neural networks in Indonesia

Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They are elevation, slope, curvature, topographical wetness index (TWI), Euclidean distance from a river, land-cover, stream power index (SPI), soil type and precipitation. This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict flood susceptibility in Jakarta for the January 2020 historic flood event across 260 key locations. For each location, this study uses current environmental conditions to predict flood status in the following month. Considering the imbalance between instances of flooded and non-flooded conditions, the Synthetic Minority Oversampling Technique (SMOTE) has been implemented to balance both classes in the training set. This work compares predictions from artificial neural networks (ANN), k-Nearest Neighbors algorithms (k-NN) and Support Vector Machines (SVM) against a random baseline. The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets. The ANN is found to be superior to the other machine learning models.

Ketersediaan
258551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
8 hlm PDF, 1.503 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
CNN
Segmentation
Radar remote sensing
Topography
DEM
Flood
Water
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Flood susceptibility assessment using artificial neural networks in Indonesia
    Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They are elevation, slope, curvature, topographical wetness index (TWI), Euclidean distance from a river, land-cover, stream power index (SPI), soil type and precipitation. This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict flood susceptibility in Jakarta for the January 2020 historic flood event across 260 key locations. For each location, this study uses current environmental conditions to predict flood status in the following month. Considering the imbalance between instances of flooded and non-flooded conditions, the Synthetic Minority Oversampling Technique (SMOTE) has been implemented to balance both classes in the training set. This work compares predictions from artificial neural networks (ANN), k-Nearest Neighbors algorithms (k-NN) and Support Vector Machines (SVM) against a random baseline. The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets. The ANN is found to be superior to the other machine learning models.
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