Journal
GEOCARTO INTERNATIONAL
Volume 37, Issue 19, Pages 5716-5741Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2021.1920629
Keywords
Urban planning; artificial intelligence; neural networks; boosting; GIS
Categories
Funding
- Maa-ja vesitekniikan tuki r.y. (MVTT)
Ask authors/readers for more resources
A new hybrid machine learning algorithm called MultiB-MLPNN was developed for urban flood susceptibility mapping and tested in Amol City, Iran, showing the best predictive performance. The model is useful for generating realistic flood susceptibility maps in data-scarce urban areas, aiding in developing risk-reduction measures to protect urban areas from devastating floods.
In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available