Flood Susceptibility Mapping with Machine Learning, Multi-Criteria Decision Analysis and Ensemble Using Dempster Shafer Theory

Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilo, Khalil Gholamnia, Omid Ghorbanzadeh, Omid Rahmati, Thomas Blaschke

Publikation: Beitrag in FachzeitschriftArtikel

Abstract

Floods are one of the most widespread natural hazards occurring across the globe. The main objective of this study was to produce flood susceptibility maps for the province of Salzburg, Austria, using two multi-criteria decision analysis (MCDA) models including analytical hierarchical process (AHP) and analytical network process (ANP) and two machine learning (ML) models including random forest (RF) and support vector machine (SVM). Additionally, we compare which of the MCDA and ML models are better suited for flood susceptibility and evaluate the use of Dempster Shafer theory (DST) for optimizing the resulting flood susceptibility maps based on eleven flood conditioning factors: elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalised difference vegetation index (NDVI), geology, rainfall, land cover, distance to roads and distance to drainage. The accuracy evaluation of the flood susceptibility maps through the AUC (area under the receiver operating characteristic curve) method along with the relative flood density (R-Index) shows that RF (AUC=87.8%) and SVM (AUC=87%) outperform the ANP (AUC=86.6%) and AHP (AUC=85.9%) models. Therefore, the predictive performance of ML models was slightly better than the MCDA models. The DST could further increase the accuracy of both ML models (AUC=88.3%) and MCDA models (AUC=87.3%). However, the best accuracy (AUC=89.3%) is reached through an ensemble of all four models.
OriginalspracheEnglisch
FachzeitschriftJournal of Hydrology
DOIs
PublikationsstatusVeröffentlicht - 8 Jul 2020

Systematik der Wissenschaftszweige 2012

  • 105 Geowissenschaften

Zitieren