Journal
GEOCARTO INTERNATIONAL
Volume 37, Issue 25, Pages 7462-7487Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2021.1974959
Keywords
Machine learning algorithms; LightGBM; CatBoost; random forest; flash flood susceptibility mapping; Wadi System
Categories
Funding
- JSPS [20KK0094]
- Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- Grants-in-Aid for Scientific Research [20KK0094] Funding Source: KAKEN
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This study introduces the use of LightGBM and CatBoost machine learning models for predicting flash flood susceptibility in the Wadi System in Hurghada, Egypt. The results demonstrate that LightGBM outperforms other models in terms of classification metrics and processing time.
This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.
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