TY  - JOUR
A1  - Rodrigo Comino, Jesús
A1  - Javidan, Narges
A1  - Kavian, Ataollah
A1  - Pourghasemi, Hamid Reza
A1  - Conoscenti, Christian
A1  - Jafarian, Zeinab
T1  - Evaluation of multi-hazard map produced using MaxEnt machine learning technique
T2  - Scientific Reports
N2  - Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.
KW  - Provinz Golestan
KW  - Überflutung
KW  - Rutschung
KW  - Bodenerosion
KW  - Naturgefahr
KW  - Risikomanagement
KW  - Karte
KW  - Maschinelles Lernen
Y1  - 2021
UR  - https://ubt.opus.hbz-nrw.de/frontdoor/index/index/docId/1777
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:385-1-17775
PB  - Springer Nature
CY  - London
ER  -