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CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı

Year 2023, Volume: 13 Issue: 2, 1067 - 1084, 01.06.2023
https://doi.org/10.21597/jist.1225104

Abstract

Bu çalışmada Kentucky Nehri havzasında son yirmi yılda meydana gelen ani sel baskınları kayıtlarına dayanarak makine öğrenmesi yöntemleri kullanılarak taşkın tehlike haritalamasının yapılması amaçlanmıştır. Tahminlerin gerçekleştirilebilmesi için yaygın olarak kullanılan ve pratik bir algoritma olan rastgele orman (RF) yöntemi kullanılmıştır. Ayrıca, bu yöntemin içsel parametreleri (ağaç sayısı ve maksimum ağaç derinliği) ise parçacık sürü optimizasyonu (PSO) algoritması ile optimize edilmiştir. Bu bağlamda 343 adet geçmiş ani sel kayıtlarına ilaveten havza sınırları içerisinde yer alacak şekilde aynı sayıda rastgele nokta atanmıştır. Tüm bu noktalara 12 adet ani sel tehlikesini tetikleyecek faktörler tanıtılmış olup, tahminler bu doğrultuda gerçekleştirilmiştir. Tahmin sonuçları birçok performans değerlendirme indikatörü göz önüne alınarak analiz edildiğinde melez PSO-RF modelinin test veri setinde oldukça başarılı sonuçlar gösterdiği görülmüştür. Öyle ki hem ani sel olan noktalar hem de ani sel gerçekleşmeyen noktalar %70 oranında doğruluk ile tahmin edilmiştir. Yapılan detaylı değerlendirmeler sonucu ise ikili sınıflandırma problemlerinde önemli bir gösterge olan AUROC değeri ise 0.79 olarak hesaplanmıştır. Ayrıca, ani selleri tetikleyen faktörlerin sonuçlar üzerindeki tekil etkileri incelendiğinde şiddetli yağış faktörü en etkili değişken olarak bulunmuş olup, onu sırasıyla topoğrafya, NDVI ve eğri numarası faktörleri izlemiştir. Öte yandan, litoloji faktörünün ani sellerin modellenmesi üzerindeki etkisi ise diğer faktörlere göre oldukça az olduğu sonucuna varılmıştır. Tüm bu bulgular ışığında elde edilen sonuçlar hem taşkın tehlike haritalaması literatürüne katkı yapacak, hem de ilgili bölgede yaşanacak gelecek ani sel olayları meydana gelmeden alınması gereken tedbirler ile ilgili yol gösterici nitelikte olacaktır.

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Incorporating the GIS-Based Hybrid Machine Learning Applications into the Flash Flood Susceptibility Mapping

Year 2023, Volume: 13 Issue: 2, 1067 - 1084, 01.06.2023
https://doi.org/10.21597/jist.1225104

Abstract

This study chiefly aimed to perform flash flood susceptibility mapping by means of machine learning methods based on the records attained in the Kentucky River basin over the last two decades. To carry out analysis, one of the widely adopted practical tree-based machine learning tools, i.e., the random forest (RF) method, was utilized, while the hyperparameters (i.e., number of trees and maximum tree depth) of the RF algorithm were tuned via the particle swarm optimization (PSO) strategy. In this vein, a total of 343 flash-flooded and the same number of random (non-flash flooded) points were assigned within the Kentucky River basin boundaries. In addition, a total of 12 factors triggering flash floods have been introduced to the corresponding points and the predictions were conducted in this regard. Many performance evaluation indicators considered within the scope of this study illustrated that the hybrid PSO-RF model revealed quite accurate predictive results based on the blinded testing set; such that both flash-flooded and non-flash flooded points exist in the test set were estimated with an accuracy of 70%. In addition, one of the promising performance indicators in assessing binary classification implementations, called AUROC, was calculated as 0.79. Further analysis regarding the individual impacts of the triggering factors also highlighted that the heavy rainfall probability factor was found to be the most effective variable, followed by topography, NDVI, and curve number, respectively. On the other hand, it was concluded that the effect of the lithology on the flash flood modeling is considerably lower compared to its counterparts. Overall, the results acquired in the light of all these findings have important potential in terms of both contributing to the flood susceptibility mapping literature and guiding with respect to the measures that should be taken prior to the flash flood incidents in the corresponding region.

References

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  • Costache, R., Hong, H., & Pham, Q. B. (2020). Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Science of the Total Environment, 711, 134514. https://doi.org/10.1016/j.scitotenv.2019.134514
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  • Darabi, H., Torabi Haghighi, A., Rahmati, O., Jalali Shahrood, A., Rouzbeh, S., Pradhan, B., & Tien Bui, D. (2021). A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation. Journal of Hydrology, 603(PA), 126854. https://doi.org/10.1016/j.jhydrol.2021.126854
  • Ekmekcioğlu, Ö., 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, 0(0), 1–19. https://doi.org/10.1080/19648189.2020.1805024
  • Ekmekcioğlu, Ö., Koc, K., & Özger, M. (2021). Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey. International Journal of Disaster Risk Reduction, 60(May). https://doi.org/10.1016/j.ijdrr.2021.102327
  • Ekmekcioğlu, Ö., & Koc, K. (2022). Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards. CATENA, 216, 106379. https://doi.org/10.1016/j.catena.2022.106379
  • Ekmekcioğlu, Ö., Koc, K., Özger, M., & Işık, Z. (2022). Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United States. Journal of Hydrology, 610, 127877. https://doi.org/10.1016/j.jhydrol.2022.127877
  • Fang, Z., Wang, Y., Peng, L., & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers and Geosciences, 139(February), 104470. https://doi.org/10.1016/j.cageo.2020.104470
  • Gigović, L., Pamučar, D., Bajić, Z., & Drobnjak, S. (2017). Application of GIS-interval rough AHP methodology for flood hazard mapping in Urban areas. Water (Switzerland), 9(6), 1–26. https://doi.org/10.3390/w9060360
  • Goswami, S., Murthy, C. A., & Das, A. K. (2018). Sparsity measure of a network graph: Gini index. Information Sciences, 462, 16–39. https://doi.org/10.1016/j.ins.2018.05.044
  • Habba, M., Ameur, M., & Jabrane, Y. (2018). A novel Gini index based evaluation criterion for image segmentation. Optik, 168, 446–457. https://doi.org/10.1016/j.ijleo.2018.04.045
  • Hou, C., Xie, Y., & Zhang, Z. (2022). An improved convolutional neural network based indoor localization by using Jenks natural breaks algorithm. China Communications, 19(4), 291–301. https://doi.org/10.23919/JCC.2022.04.021
  • Ikeuchi, H., Hirabayashi, Y., Yamazaki, D., Muis, S., Ward, P. J., Winsemius, H. C., … Kanae, S. (2017). Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh. Journal of Advances in Modeling Earth Systems, 9(4), 1847–1862. https://doi.org/10.1002/2017MS000943
  • Jaafar, H. H., Ahmad, F. A., & El Beyrouthy, N. (2019). GCN250, new global gridded curve numbers for hydrologic modeling and design. Scientific Data, 6(1), 1–9. https://doi.org/10.1038/s41597-019-0155-x
  • Liu, X., Zhang, Z., Jiang, T., Li, X., & Li, Y. (2021). Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China. Journal of Spectroscopy, 2021, 1–17. https://doi.org/10.1155/2021/5957376
  • Long, Y., Song, Y., & Chen, L. (2022). Identifying subcenters with a nonparametric method and ubiquitous point-of-interest data: A case study of 284 Chinese cities. Environment and Planning B: Urban Analytics and City Science, 49(1), 58–75. https://doi.org/10.1177/2399808321996705
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There are 48 citations in total.

Details

Primary Language Turkish
Subjects Civil Engineering
Journal Section İnşaat Mühendisliği / Civil Engineering
Authors

Ömer Ekmekcioğlu 0000-0002-7144-2338

Early Pub Date May 27, 2023
Publication Date June 1, 2023
Submission Date December 27, 2022
Acceptance Date February 21, 2023
Published in Issue Year 2023 Volume: 13 Issue: 2

Cite

APA Ekmekcioğlu, Ö. (2023). CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı. Journal of the Institute of Science and Technology, 13(2), 1067-1084. https://doi.org/10.21597/jist.1225104
AMA Ekmekcioğlu Ö. CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı. J. Inst. Sci. and Tech. June 2023;13(2):1067-1084. doi:10.21597/jist.1225104
Chicago Ekmekcioğlu, Ömer. “CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı”. Journal of the Institute of Science and Technology 13, no. 2 (June 2023): 1067-84. https://doi.org/10.21597/jist.1225104.
EndNote Ekmekcioğlu Ö (June 1, 2023) CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı. Journal of the Institute of Science and Technology 13 2 1067–1084.
IEEE Ö. Ekmekcioğlu, “CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı”, J. Inst. Sci. and Tech., vol. 13, no. 2, pp. 1067–1084, 2023, doi: 10.21597/jist.1225104.
ISNAD Ekmekcioğlu, Ömer. “CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı”. Journal of the Institute of Science and Technology 13/2 (June 2023), 1067-1084. https://doi.org/10.21597/jist.1225104.
JAMA Ekmekcioğlu Ö. CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı. J. Inst. Sci. and Tech. 2023;13:1067–1084.
MLA Ekmekcioğlu, Ömer. “CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı”. Journal of the Institute of Science and Technology, vol. 13, no. 2, 2023, pp. 1067-84, doi:10.21597/jist.1225104.
Vancouver Ekmekcioğlu Ö. CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı. J. Inst. Sci. and Tech. 2023;13(2):1067-84.