Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-05-04

AUTHORS

Farhana Parvin, Sk Ajim Ali, Beata Calka, Elzbieta Bielecka, Nguyen Thi Thuy Linh, Quoc Bao Pham

ABSTRACT

Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies. More... »

PAGES

1-21

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    http://scigraph.springernature.com/pub.10.1007/s00704-022-04068-7

    DOI

    http://dx.doi.org/10.1007/s00704-022-04068-7

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