Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

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ID: 111670
2020
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Abstract
This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.
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costache2020waternovel Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Romulus Costache;Phuong Thao Thi Ngo;Dieu Tien Bui;Costache, Romulus;Ngo, Phuong Thao Thi;Bui, Dieu Tien;
Journal water
Year 2020
DOI
10.3390/w12061549
URL
https://www.mdpi.com/2073-4441/12/6/1549
Keywords
Deep learning Romania statistical learning spatial modeling flash flood

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