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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms

Year 2024, Volume: 10 Issue: 1, 1 - 15, 28.01.2024
https://doi.org/10.21324/dacd.1254778

Abstract

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.

References

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İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması

Year 2024, Volume: 10 Issue: 1, 1 - 15, 28.01.2024
https://doi.org/10.21324/dacd.1254778

Abstract

Bu çalışma kapsamında meydana gelebilecek olası bir sel olayının gerçekleşebileceği yerin önceden tahmini ve tespiti için makine öğrenmesi yöntemleri kullanılarak coğrafi bilgi sistemleri (CBS) tabanlı bir sel duyarlılık haritalama modeli oluşturulması amaçlanmıştır. Çalışma kapsamında incelen bölge olarak ise Türkiye’nin metropol kenti olan İstanbul ili seçilmiştir. Literatürden elde edilen sel envanteriyle oluşturulan örneklem kümesi önce sel olmayan noktaların rastgele oluşturulması ile genişletilmiş olup, ardından sınıf dengesizliği rastgele alt örnekleme (RUS) tekniği ile giderilmiştir. Bu yaklaşım Türkiye’ de gerçekleştirilen sel duyarlılık haritalamaları çalışmaları için ilk kez uygulanmıştır. Rastgele orman (RF), stokastik gradyan artırma (SGB) ve XGBoost algoritmaları olmak üzere üç farklı makine öğrenmesi algoritmasının performans karşılaştırmaları gerçekleştirilmiştir. En yüksek model performansının XGBoost ile elde edildiği, bu metodu ise sırasıyla SGB ve RF’nin takip ettiği sonucuna ulaşılmıştır. Ayrıca, RF ve SGB modellerinin sel olmayan noktaların neredeyse tamamını doğru olarak bulduğu, sel olan noktalarda ise %90.67’lik bir başarı sergilediği görülmüştür. Fakat, çalışmanın esas amacını kapsayan sel gerçekleşen noktaların belirlenmesinde XGBoost modeli %92.00’lik bir başarı ile diğer iki metoda üstünlük sergilediği tespit edilmiştir. Sel olayını etkileyen parametreler incelendiğinde ise İstanbul için seli en önemli parametrenin yağış olduğu sonucuna ulaşılmış olup, yağışı sırasıyla drenaj ağına uzaklık ve eğri numarası takip etmiştir. Sonuç olarak çalışma kapsamında İstanbul’da gerçekleştirilen sel duyarlılık haritalamaları çalışmaları için ilk kez uygulanan bu çerçevenin kullanımının sayısı ve etkileri giderek artırılarak sel olaylarına karşı daha yaygın alanlara uygulanması gelecek vadedici bir yaklaşım olacaktır.

References

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  • Anılan, T., Durmuş, H., Akçalı, E., & Yüksek, M. (2021). Taşkın farkındalık ve erken uyarı sistemleri değerlendirmesi: Trabzon Beşikdüzü örneği. Doğal Afetler ve Çevre Dergisi, 7(1), 110–123. https://doi.org/10.21324/dacd.722798
  • Avand, M., Khiavi, A. N., Khazaei, M., & Tiefenbacher, J. P. (2021). Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning. Journal of Environmental Management, 295, Article 113040. https://doi.org/10.1016/j.jenvman.2021.113040
  • Aydin, H. E., & Iban, M. C. (2022). Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. Natural Hazards, 116(3), 2957–2991. https://doi.org/10.1007/s11069-022-05793-y
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  • Bhattacharya, S., S, S. R. K., Maddikunta, P. K. R., Kaluri, R., Singh, S., Gadekallu, T. R., Alazab, M., & Tariq, U. (2020). A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU. Electronics, 9(2), Article 219. https://doi.org/10.3390/electronics9020219
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
  • Chakrabortty, R., Chandra Pal, S., Rezaie, F., Arabameri, A., Lee, S., Roy, P., Saha, A., Chowdhuri, I., & Moayedi, H. (2021). Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India. Geocarto International, 37(23), 6713–6735. https://doi.org/10.1080/10106049.2021.1953618
  • Chen, T., & Guestrin, C. (2016). XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. https://doi.org/10.1145/2939672.2939785
  • Choi, J., Gu, B., Chin, S., & Lee, J. S. (2020). Machine learning predictive model based on national data for fatal accidents of construction workers. Automation in Construction, 110, Article 102974. https://doi.org/10.1016/j.autcon.2019.102974
  • Costache, R., Pham, Q. B., Avand, M., Thuy Linh, N. T., Vojtek, M., Vojteková, J., Lee, S., Khoi, D. N., Thao Nhi, P. T., & Dung, T. D. (2020). Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment. Journal of Environmental Management, 265, Article 110485. https://doi.org/10.1016/j.jenvman.2020.110485
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  • Deroliya, P., Ghosh, M., Mohanty, M. P., Ghosh, S., Rao, K. D., & Karmakar, S. (2022). A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions. Science of the Total Environment, 851, Article 158002. https://doi.org/10.1016/j.scitotenv.2022.158002
  • Devi, K. K., and Kumar, G. A. S. (2022). Stochastic gradient boosting model for twitter spam detection. Computer Systems Science and Engineering, 41 (2), 849–859. https://doi.org/10.32604/csse.2022.020836
  • Ekmekcioğlu, M., Başakın, E. E., & Özger, M. (2020). Tree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways. European Journal of Environmental and Civil Engineering, 26(8), 3547–3565. https://doi.org/10.1080/19648189.2020.1805024
  • Ekmekcioğlu, M., & Koc, K. (2022a). Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards. Catena, 216, Article 106379. https://doi.org/10.1016/j.catena.2022.106379
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There are 55 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Zehra Koyuncu 0000-0002-2087-8991

Ömer Ekmekcioğlu 0000-0002-7144-2338

Publication Date January 28, 2024
Submission Date February 22, 2023
Acceptance Date September 4, 2023
Published in Issue Year 2024Volume: 10 Issue: 1

Cite

APA Koyuncu, Z., & Ekmekcioğlu, Ö. (2024). İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması. Doğal Afetler Ve Çevre Dergisi, 10(1), 1-15. https://doi.org/10.21324/dacd.1254778
AMA Koyuncu Z, Ekmekcioğlu Ö. İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması. J Nat Haz Environ. January 2024;10(1):1-15. doi:10.21324/dacd.1254778
Chicago Koyuncu, Zehra, and Ömer Ekmekcioğlu. “İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması”. Doğal Afetler Ve Çevre Dergisi 10, no. 1 (January 2024): 1-15. https://doi.org/10.21324/dacd.1254778.
EndNote Koyuncu Z, Ekmekcioğlu Ö (January 1, 2024) İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması. Doğal Afetler ve Çevre Dergisi 10 1 1–15.
IEEE Z. Koyuncu and Ö. Ekmekcioğlu, “İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması”, J Nat Haz Environ, vol. 10, no. 1, pp. 1–15, 2024, doi: 10.21324/dacd.1254778.
ISNAD Koyuncu, Zehra - Ekmekcioğlu, Ömer. “İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması”. Doğal Afetler ve Çevre Dergisi 10/1 (January 2024), 1-15. https://doi.org/10.21324/dacd.1254778.
JAMA Koyuncu Z, Ekmekcioğlu Ö. İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması. J Nat Haz Environ. 2024;10:1–15.
MLA Koyuncu, Zehra and Ömer Ekmekcioğlu. “İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması”. Doğal Afetler Ve Çevre Dergisi, vol. 10, no. 1, 2024, pp. 1-15, doi:10.21324/dacd.1254778.
Vancouver Koyuncu Z, Ekmekcioğlu Ö. İstanbul İçin CBS Tabanlı Makine Öğrenmesi İle Sel Duyarlılık Haritasının Oluşturulması. J Nat Haz Environ. 2024;10(1):1-15.