TY - GEN
T1 - Development of Flood Inundation Mapping by Using Optimized Deep Learning Model
AU - Halisno, Nico
AU - Solihin, Mahmud Iwan
AU - Nasser, Eriko Nasemudin
AU - Syafrudin, A. Hadi
AU - MacHmudah, Affiani
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Bayesian optimization
KW - deep learning
KW - flood inundation mapping
KW - hyperband
KW - hyperparameter tuning
KW - multispectral remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85214688691&partnerID=8YFLogxK
U2 - 10.1109/ICARES64249.2024.10768067
DO - 10.1109/ICARES64249.2024.10768067
M3 - Conference contribution
AN - SCOPUS:85214688691
T3 - 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024 - Proceedings
BT - 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2024
Y2 - 8 November 2024 through 9 November 2024
ER -