Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Quoc Bao Pham (), Sk Ajim Ali, Elzbieta Bielecka, Beata Calka, Agata Orych, Farhana Parvin and Ewa Łupikasza
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Quoc Bao Pham: University of Silesia in Katowice
Sk Ajim Ali: Aligarh Muslim University (AMU)
Elzbieta Bielecka: Military University of Technology
Beata Calka: Military University of Technology
Agata Orych: Military University of Technology
Farhana Parvin: Aligarh Muslim University (AMU)
Ewa Łupikasza: University of Silesia in Katowice

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 2, No 10, 1043-1081

Abstract: Abstract Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management.

Keywords: Flood modelling; Deep learning neural networks (DLNNs); Artificial neural network (ANN); Geographic information system (GIS); Bayesian Logistic Regression (BLR); Analytical hierarchy process (AHP); Flood exposure (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05336-5

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