TY - JOUR
T1 - Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network
T2 - Case Study of Kulmbach
AU - Lin, Qing
AU - Leandro, Jorge
AU - Wu, Wenrong
AU - Bhola, Punit
AU - Disse, Markus
N1 - Publisher Copyright:
© Copyright © 2020 Lin, Leandro, Wu, Bhola and Disse.
PY - 2020/8/5
Y1 - 2020/8/5
N2 - In many countries, floods are the leading natural disaster in terms of damage and losses per year. Early prediction of such events can help prevent some of those losses. Artificial neural networks (ANN) show a strong ability to deal quickly with large amounts of measured data. In this work, we develop an ANN for outputting flood inundation maps based on multiple discharge inputs with a high grid resolution (4 m × 4 m). After testing different neural network training algorithms and network structures, we found resilience backpropagation to perform best. Furthermore, by introducing clustering for preprocessing discharge curves before training, the quality of the prediction could be improved. Synthetic flood events are used for the training and validation of the ANN. Historical events were additionally used for further validation with real data. The results show that the developed ANN is capable of predicting the maximum flood inundation extents. The mean squared error in more than 98 and 86% of the total area is smaller than 0.2 m2 in the prediction of synthetic events and historical events, respectively.
AB - In many countries, floods are the leading natural disaster in terms of damage and losses per year. Early prediction of such events can help prevent some of those losses. Artificial neural networks (ANN) show a strong ability to deal quickly with large amounts of measured data. In this work, we develop an ANN for outputting flood inundation maps based on multiple discharge inputs with a high grid resolution (4 m × 4 m). After testing different neural network training algorithms and network structures, we found resilience backpropagation to perform best. Furthermore, by introducing clustering for preprocessing discharge curves before training, the quality of the prediction could be improved. Synthetic flood events are used for the training and validation of the ANN. Historical events were additionally used for further validation with real data. The results show that the developed ANN is capable of predicting the maximum flood inundation extents. The mean squared error in more than 98 and 86% of the total area is smaller than 0.2 m2 in the prediction of synthetic events and historical events, respectively.
KW - artificial neural network
KW - hazard
KW - maximum flood inundation extent
KW - resilient backpropagation
KW - urban flood forecast
UR - http://www.scopus.com/inward/record.url?scp=85089816081&partnerID=8YFLogxK
U2 - 10.3389/feart.2020.00332
DO - 10.3389/feart.2020.00332
M3 - Article
AN - SCOPUS:85089816081
SN - 2296-6463
VL - 8
JO - Frontiers in Earth Science
JF - Frontiers in Earth Science
M1 - 332
ER -