Predicting flood-prone areas using generalized linear and maximum entropy machine learning models

Document Type : Original Article

Authors

1 PH.D. Student of Watershed Management, Urmia University, Iran

2 Associate Professor, Department of Range and Watershed Management, Urmia University, Iran

Abstract

The purpose of this study is to identify the effective factors, prepare flood risk prediction maps using machine learning models, and finally evaluate the efficiency of these models in the Zive watershed of Urmia. For this purpose, environmental and human factors including morphometric indices; Waterway Power Index (SPI), Slope Length Index (LS), Topographic Wetness Index (TWI), Topographic Position Index (TPI), Land Roughness Index (TRI), Mass Balance Index (MBI), Profile Curvature Index and The surface curvature index (Plan Curvature), rainfall, basin height, slope degree, slope direction, lithology, land use, normalized difference index of vegetation cover (NDVI), distance from waterway, distance from village and distance from fault were used. For this purpose, 96 flood spots were identified in the basin by using field visits and Google Earth images and sources received from the offices. Layers related to morphometric indices from the digital height model (12.5 x 12.5) meters and in the SAGA_GIS environment; And maps of environmental and human factors were prepared and digitized in the ArcGIS geographic information system. The evaluation results of two models using the ROC curve for machine learning (ML) models showed that the maximum entropy model with AUC=0.916 and the generalized linear model with AUC=0.902 have excellent performance in the field The results of the Kappa index for the superior model showed that environmental factors including geology, distance from waterways, height and slope have the greatest impact and the least impact related to profile curvature index factors. , land use, and mass balance index. Identifying high-risk areas and determining factors affecting the occurrence of floods in this basin can be very efficient in reducing possible damages.

Keywords

Main Subjects

References

References (in Persian)
Abedini, Mussa., Faal Naziri, Mehdi., & Pirouzi, Alnaz. (2023). Flood risk assessment and zoning using multi-criteria Ara's technique and single hydrograph (Case Study: Upstream Basin of Soltan Meshkinshahr Bridge Hydrometric Station). Journal of Natural Environmental Hazards, 12(35), 115-138. 10.22111/JNEH.2022.40684.1863. [In Persian]  
Bakhtiari, Mohsen., & Jahantab, Zahra. (2022). Spatial modeling of floods using Artificial Neural Network (ANN) and analyst functions of GIS. Journal of Climate Research, (49), 177-194. [In Persian] 
Dastranj, Ali., & Karimi, Ebrahim. (2022). Landslide susceptibility prediction using the maximum entropy machine learning algorithm (Bar catchment of Nishapur). Researches in Earth Sciences, 13(3), 76-96. https://doi.org/10.48308/esrj.2022.102965. [In Persian]  
Esmaili, Reza., & Taheri, Mohammad. (2022). Evaluation of flood hazard areas with fuzzy approach, Case study: Downstream of Neka catchment, Mazandaran province. Journal of Natural Environmental Hazards, 11(34), 145-158. 10.22111/JNEH.2022.39817.1842. [In Persian]  
Hanifinia, Abdulaziz., & Abghari, Hirad. (2022). Investigation of affecting factors landslide occurrence in Shannon entropy model with two WOE and LNRF approaches to zoning sensitivity landslide in Ziveh watershed of Urmia. Quantitative Geomorphological Research, 11(2), 108-127. 10.22034/gmpj.2022.340292.1348. [In Persian]  
Hanifinia, Abdulaziz., & Nazarnejad, Habib. (2022). The Effect of Morphometry Indices on Improving the Performance of Data Mining Models for Landslide Sensitivity Zoning in Cherikabad Watershed, Urmia. Journal of Geography and Environmental Hazards, 10(4), 47-68. 10.22067/GEOEH.2021.69707.1041. [In Persian]  
Hanifinia, Abdulaziz., Nazarnejad, Habib., Najafi, Saeed., & Kornejady, Aiding. (2020). Prioritization of Effective Factors on Landslide Occurrence and Mapping of its Sensitivity in CherikAbad Watershed, Urmia Using Shannon Entropy Model. Watershed Management Research Journal, 33(4), 30-46. 10.22092/WMEJ.2020.128407.1280. [In Persian]  
Hassanzadeh, Reza., Honarmand, Mahdi., Hossinjanizadeh, Mahdieh., & Mohammadi, Sedighe. (2021). Flood zoning in urban areas using hydrological modeling and survey data: A case study of Bardsir city, Kerman Province. Iranian Journal of Ecohydrology, 8(2), 331-344. 10.22059/IJE.2021.314075.1423. [In Persian]  
Teimoori, Mehdi., Vakili tajareh, Farzaneh., Mozayyan, Malihe., Ramezani, Marziyeh (2023). Comparison of Machine Learning Models in Flood Susceptibility Zoning in Karaj Dam Basin. jwmseir; 17 (61) :30-40. 20.1001.1.20089554.1402.17.61.4.8 [In Persian]
 
References (in English)
Anand, A. K., & Pradhan, S. P. (2023). Evaluation of bivariate statistical and hybrid models for the preparation of flood hazard susceptibility maps in the Brahmani River Basin, India. Environmental Earth Sciences, 82(16), 389.
Avand, M., Kuriqi, A., Khazaei, M., & Ghorbanzadeh, O. (2022). DEM resolution effects on machine learning performance for flood probability mapping. Journal of Hydro-Environment Research, 40, 1-16.
Baig, S. U., Rehman, M. U., & Janjua, N. N. (2021). District-level disaster risk and vulnerability in the Northern mountains of Pakistan. Geomatics, Natural Hazards and Risk, 12(1), 2002-2022.
Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866.
Boussouf, S., Fernández, T., & Hart, A. B. (2023). Landslide susceptibility mapping using maximum entropy (MaxEnt) and geographically weighted logistic regression (GWLR) models in the Río Aguas catchment (Almería, SE Spain). Natural Hazards, 117(1), 207-235.
Bui, D. T., Tsangaratos, P., Ngo, P. T. T., Pham, T. D., & Pham, B. T. (2019). Flash flood susceptibility modeling using an optimized fuzzy rule-based feature selection technique and tree-based ensemble methods. Science of the total environment, 668, 1038-1054.
Davoudi Moghaddam, D., Pourghasemi, H. R., & Rahmati, O. (2019). Assessment of the contribution of geo-environmental factors to flood inundation in a semi-arid region of SW Iran: Comparison of different advanced modeling approaches. Natural hazards GIS-based spatial modeling using data mining techniques, 59-78.‏
El-Rawy, M., Elsadek, W. M., & De Smedt, F. (2023). Flood hazard assessment and mitigation using a multi-criteria approach in the Sinai Peninsula, Egypt. Natural Hazards, 115(1), 215-236.
Ha, H., Luu, C., Bui, Q. D., Pham, D. H., Hoang, T., Nguyen, V. P., ... & Pham, B. T. (2021). Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural hazards, 109(1), 1247-1270.
Hirabayashi, Yukiko, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim, and Shinjiro Kanae. "Global flood risk under climate change." Nature Climate Change 3, no. 9 (2013): 816-821.
Huang, K., Li, X., Liu, X., & Seto, K. C. (2019). Projecting global urban land expansion and heat island intensification through 2050. Environmental Research Letters, 14(11), 114037.
Islam, A. R. M. T., Bappi, M. M. R., Alqadhi, S., Bindajam, A. A., Mallick, J., & Talukdar, S. (2023). Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries. Natural Hazards, 119(1), 1-37.
Khosravi, K., Panahi, M., Golkarian, A., Keesstra, S. D., Saco, P. M., Bui, D. T., & Lee, S. (2020). Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology, 591, 125552.
Möller M, Volk M, Friedrich K, Lymburner L. (2008). Placing soil-genesis and transport processes into a landscape context: A multiscale terrain-analysis approach, Journal of Plant Nutrition and Soil Science 171 (3): 419-430.
Mosavi, A., Golshan, M., Janizadeh, S., Choubin, B., Melesse, A. M., & Dineva, A. A. (2022). Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto International, 37(9), 2541-2560.
Panahi, M., Jaafari, A., Shirzadi, A., Shahabi, H., Rahmati, O., Omidvar, E., ... & Bui, D. T. (2021). Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers, 12(3), 101076.
Pham, B. T., Luu, C., Van Phong, T., Trinh, P. T., Shirzadi, A., Renoud, S., ... & Clague, J. J. (2021). Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology, 592, 125615.
Riazi, M., Khosravi, K., Shahedi, K., Ahmad, S., Jun, C., Bateni, S. M., & Kazakis, N. (2023). Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms. Science of The Total Environment, 871, 162066.
Saha, S., Saha, A., Hembram, T. K., Mandal, K., Sarkar, R., & Bhardwaj, D. (2022). Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM, and random forest in the Indian Himalayan region. Stochastic Environmental Research and Risk Assessment, 36(10), 3597-3616.
Samantaray, S., Agnihotri, A., & Sahoo, A. (2023). Flood Replication Using ANN Model Concerning Various Catchment Characteristics: Narmada River Basin. Journal of The Institution of Engineers (India): Series A, 104(2), 381-396.
Shahabi H, Khezri S, Ahmad BB, Hashim M. (2014). Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio, and logistic regression models, Catena 115: 55-70.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., & Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927-941.
Sofia, G., Roder, G., Dalla Fontana, G., & Tarolli, P. (2017). Flood dynamics in urbanized landscapes: 100 years of climate and humans’ interaction. Scientific reports, 7(1), 40527.
Tabari, H. (2020). Climate change's impact on floods and extreme precipitation increases with water availability. Scientific reports, 10(1), 13768.
UN Office for Disaster Risk Reduction. The Human Cost of Disasters: An Overview of the Last 20 Years (2000–2019); UN Office for Disaster Risk Reduction Geneva: Geneva, Switzerland, 2020.
Willis, G. M., Ward, T., & Levenson, J. S. (2014). The Good Lives model (GLM) is an evaluation of GLM operationalization in North American treatment programs. Sexual Abuse, 26(1), 58-81.
Xiong, F., Guo, S., Chen, L., Chang, F. J., Zhong, Y., & Liu, P. (2018). Identification of flood seasonality using an entropy-based method. Stochastic Environmental Research and Risk Assessment, 32, 3021-3035.
Xu, A., Chang, H., Xu, Y., Li, R., Li, X., & Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385-402.
Youssef, A. M., Pourghasemi, H. R., & El-Haddad, B. A. (2022). Advanced machine learning algorithms for flood susceptibility modeling—Performance comparison: Red Sea, Egypt. Environmental Science and Pollution Research, 29(44), 66768-66792.
Youssef, A. M., Pourghasemi, H. R., Mahdi, A. M., & Matar, S. S. (2023). Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models. Environmental Science and Pollution Research, 30(6), 16081-16105.

Files

History

  • Receive Date: 14 January 2024
  • Revise Date: 13 May 2024
  • Accept Date: 08 June 2024

Share

How to cite

Statistics