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Journal Article

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

Authors

Costache,  Romulus
External Organizations;

Pham,  Quoc Bao
External Organizations;

/persons/resource/esharifi

Sharifi,  Ehsan
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Linh,  Nguyen Thi Thuy
External Organizations;

Abba,  S.I.
External Organizations;

Vojtek,  Matej
External Organizations;

Vojteková,  Jana
External Organizations;

Nhi,  Pham Thi Thao
External Organizations;

Khoi,  Dao Nguyen
External Organizations;

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Citation

Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S., Vojtek, M., Vojteková, J., Nhi, P. T. T., Khoi, D. N. (2020): Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. - Remote Sensing, 12, 1, 106.
https://doi.org/10.3390/rs12010106


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000625
Abstract
Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.