Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms
The main objective of the current study is to generate a flood hazard map by using the machine learning algorithms hybridized with the geographic information systems (GIS). In this regard, the province of Istanbul, which is the metropolitan city of Turkey, was selected as the focal region within the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Artvin Coruh University
2024-01-01
|
| Series: | Doğal Afetler ve Çevre Dergisi |
| Subjects: | |
| Online Access: | http://dacd.artvin.edu.tr/tr/download/article-file/2967276 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832544524112494592 |
|---|---|
| author | Zehra Koyuncu Ömer Ekmekcioğlu |
| author_facet | Zehra Koyuncu Ömer Ekmekcioğlu |
| author_sort | Zehra Koyuncu |
| collection | DOAJ |
| description | The main objective of the current study is to generate a flood hazard map by using the machine learning algorithms hybridized with the geographic information systems (GIS). In this regard, the province of Istanbul, which is the metropolitan city of Turkey, was selected as the focal region within the scope of the study. The class imbalance was tackled through the commonly used random under sampling (RUS) technique in order to create a fair comparison datum line. It is worth mentioning that this is the first time this approach has been used for flood hazard mapping studies in Turkey. Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. The best predictive performance was obtained with the XGBoost algorithm, followed by SGB and RF, respectively. The RF and SGB models showed a 90.67% success rate in determining the inundation points, while the XGBoost model outperformed its counterparts with a 92.00% success rate in determining the inundation points. In this research, the importance levels of the flood triggering variables were further investigated in order to enliven the comprehensibility of the obtained results. Thus, the most important variable was the precipitation, followed by the distance to the drainage network and the number of curves, respectively. Finally, it is suggested that flood vulnerability mapping attempts can be considered as promising approaches against increasing flood incidents over the years. |
| format | Article |
| id | doaj-art-09087feb2f42450cbf2f696c0c73afab |
| institution | Kabale University |
| issn | 2528-9640 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Artvin Coruh University |
| record_format | Article |
| series | Doğal Afetler ve Çevre Dergisi |
| spelling | doaj-art-09087feb2f42450cbf2f696c0c73afab2025-02-03T10:10:04ZengArtvin Coruh UniversityDoğal Afetler ve Çevre Dergisi2528-96402024-01-0110111510.21324/dacd.1254778Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning AlgorithmsZehra Koyuncu0https://orcid.org/0000-0002-2087-8991Ömer Ekmekcioğlu1https://orcid.org/0000-0002-7144-2338İnşaat Mühendisliği Bölümü, Mühendislik ve Doğa Bilimleri Fakültesi, İstanbul Medipol Üniversitesi, İstanbul.Afet ve Acil Durum Yönetimi Ana Bilim Dalı, Afet Yönetim Enstitüsü, İstanbul Teknik Üniversitesi, İstanbul.The main objective of the current study is to generate a flood hazard map by using the machine learning algorithms hybridized with the geographic information systems (GIS). In this regard, the province of Istanbul, which is the metropolitan city of Turkey, was selected as the focal region within the scope of the study. The class imbalance was tackled through the commonly used random under sampling (RUS) technique in order to create a fair comparison datum line. It is worth mentioning that this is the first time this approach has been used for flood hazard mapping studies in Turkey. Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. The best predictive performance was obtained with the XGBoost algorithm, followed by SGB and RF, respectively. The RF and SGB models showed a 90.67% success rate in determining the inundation points, while the XGBoost model outperformed its counterparts with a 92.00% success rate in determining the inundation points. In this research, the importance levels of the flood triggering variables were further investigated in order to enliven the comprehensibility of the obtained results. Thus, the most important variable was the precipitation, followed by the distance to the drainage network and the number of curves, respectively. Finally, it is suggested that flood vulnerability mapping attempts can be considered as promising approaches against increasing flood incidents over the years.http://dacd.artvin.edu.tr/tr/download/article-file/2967276geographical information systemsistanbulmachine learningrisk managementflood hazard mappingremote sensing |
| spellingShingle | Zehra Koyuncu Ömer Ekmekcioğlu Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms Doğal Afetler ve Çevre Dergisi geographical information systems istanbul machine learning risk management flood hazard mapping remote sensing |
| title | Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms |
| title_full | Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms |
| title_fullStr | Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms |
| title_full_unstemmed | Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms |
| title_short | Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms |
| title_sort | generating the flood susceptibility map for istanbul with gis based machine learning algorithms |
| topic | geographical information systems istanbul machine learning risk management flood hazard mapping remote sensing |
| url | http://dacd.artvin.edu.tr/tr/download/article-file/2967276 |
| work_keys_str_mv | AT zehrakoyuncu generatingthefloodsusceptibilitymapforistanbulwithgisbasedmachinelearningalgorithms AT omerekmekcioglu generatingthefloodsusceptibilitymapforistanbulwithgisbasedmachinelearningalgorithms |