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Inge Revhaug

Researcher at Norwegian University of Life Sciences

Publications -  24
Citations -  4187

Inge Revhaug is an academic researcher from Norwegian University of Life Sciences. The author has contributed to research in topics: Landslide & Support vector machine. The author has an hindex of 18, co-authored 24 publications receiving 3247 citations.

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Journal ArticleDOI

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

TL;DR: In this article, a landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010, and the probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period.
Journal ArticleDOI

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

TL;DR: This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management, and concludes that the proposed model is a promising alternative tool that should also be considered for forestFire susceptibility mapping in other areas.
Journal ArticleDOI

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

TL;DR: Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic model showed better result in both the success rate and prediction rate and may be useful for local planner in areas prone to landslides.
Book ChapterDOI

Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles

TL;DR: In this article, the results of decision tree classifier and its ensembles for landslide susceptibility assessment along the National Road 32 of Vietnam were compared and validated using a validation dataset not used during model building.