Share
Export Citation
APA
MLA
Chicago
Harvard
Vancouver
BIBTEX
RIS
Universitas Hasanuddin
Research output:Contribution to journal›Article›peer-review
Integrating machine learning and physically based hydrodynamic modeling for flood hazard mapping: a case study of the Takkalasi watershed, Indonesia
Soma A.S.
Geomatics Natural Hazards and Risk
Q1Published: 2026
Abstract
Sourced directly from Elsevier Scopus. No OpenAlex abstract available.
Access to Document
10.1080/19475705.2026.2659164Other files and links
- Link to publication in Scopus
- Open Access Version Available
Fingerprint
Flood mythSciences
Artificial intelligenceSciences
Computer scienceSciences
Machine learningSciences
HazardSciences
EngineeringSciences
Environmental scienceSciences
Training (meteorology)Sciences
Hazard analysisSciences
Flood forecastingSciences
Random forestSciences
Support vector machineSciences