Development of Flood Inundation Mapping by Using Optimized Deep Learning Model

Nico Halisno, Mahmud Iwan Solihin, Eriko Nasemudin Nasser, A. Hadi Syafrudin, Affiani MacHmudah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Accurate Flood Inundation Mapping (FIM) is of paramount importance for effective disaster risk reduction in the context of climate action. This paper presents an innovative approach to FIM using optimized deep learning models. Leveraging a vast historical dataset of flood events obtained from Radiant MLHub, which includes multispectral remote sensing data consisting of 12 spectral bands categorized as 'Flood' and 'No Flood,' a deep learning model is trained to predict flood extents with high accuracy. Bayesian Optimization (BO) and the Hyperband algorithm are employed during the hyperparameter tuning process of the deep learning models. Two base models are used: a Convolutional Neural Network (CNN) and the pre-trained Visual Geometry Group 16 (VGG16) model. To achieve the best performance, four scenarios-CNN-BO, CNN-Hyperband, VGG16-BO, and VGG16-Hyperband-are investigated. The results show that VGG16-Hyperband outperforms the other scenarios, achieving 87.3% validation accuracy.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331542016
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024 - Hybrid, Yogyakarta, Indonesia
Duration: 8 Nov 20249 Nov 2024

Publication series

Name2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024 - Proceedings

Conference

Conference2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period8/11/249/11/24

Keywords

  • Bayesian optimization
  • deep learning
  • flood inundation mapping
  • hyperband
  • hyperparameter tuning
  • multispectral remote sensing

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