학술논문
Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region.
Document Type
Article
Author
Source
Subject
*FLOOD warning systems
*MACHINE learning
*FLOOD risk
*SEASONS
*RANDOM forest algorithms
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Language
ISSN
1010-6049
Abstract
The main aim of this research is to predict the impact of seasonal precipitation regimes on flood hazard applying machine learning models. For this purpose, twelve static variables and eight rainfall dynamics variables for 2050s (RCP 2.6 and 8.5) were used as conditioning factors. Four machine learning algorithms including K-Nearest Neighbour (KNN), Extremely Randomize Trees (ERT), Random Forest (RF) and Oblique-Random Forest (ORF) were used to model flood risk. Considering the area under curve (AUC) and other indices, the ORF was the most optimal model. The AUC of the KNN, ERT, RF and ORF for the validation datasets were 0.85, 0.90, 0.89 and 0.92 respectively. The results showed that under two RCPs, spatial distribution of high flood risk areas will change in the future and the trends will be different from the current. These results could provide valuable insights in simulating, predicting and reducing future flood risk. [ABSTRACT FROM AUTHOR]