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Publication Detail
Flood susceptibility assessment using artificial neural networks in Indonesia
  • Publication Type:
    Journal article
  • Authors:
    Priscillia S, Schillaci C, Lipani A
  • Publisher:
    Elsevier BV
  • Publication date:
    12/2021
  • Pagination:
    215, 222
  • Journal:
    Artificial Intelligence in Geosciences
  • Volume:
    2
  • Status:
    Published
  • Print ISSN:
    2666-5441
  • Language:
    English
  • Keywords:
    Radar remote sensing, Topography, DEM, CNN, Segmentation, Flood, Water
  • Notes:
    © 2022 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Artificial Intelligence in Geosciences 2 (2021) 215–222
Abstract
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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