Flood Susceptibility Mapping Using an Ensemble of Deep Learning Method and HHO Algorithm

Document Type : Research Paper

Authors

1 Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran.

2 Department of Water Engineering, Faculty of Agricultural Technology, University of Tehran, Tehran, Iran.

10.22059/jwim.2025.392100.1212

Abstract

Predicting flood-prone areas to enhance crisis management has been a critical focus of research in recent years. The present study aims to introduce a novel and robust hybrid method for identifying flood-vulnerable zones in Tehran. For this purpose, a Convolutional Neural Network (CNN) was integrated with the Harris Hawks Optimization (HHO) algorithm to improve predictive accuracy. To compile flood inventory data, historical flood records, social media reports, and datasets from relevant organizations were analyzed, resulting in a map of 157 documented flood events. The dataset was divided into training (70%) and validation (30%) subsets. Nine influential parameters were selected as independent variables: elevation, slope, rainfall, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance from rivers, river density, geology, and land use. The model’s performance was evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sensitivity, Specificity, Accuracy, and the Area Under the ROC Curve (AUC-ROC). The AUC-ROC value demonstrated exceptional predictive accuracy (95%) for the CNN-HHO hybrid model. Furthermore, the results of other metrics—Accuracy (55.92%), Specificity (79.5%), Sensitivity (48.9%), MAE (0.165), and RMSE (0.277)—confirmed the model’s robust capability in flood zonation mapping. The findings highlight the effectiveness of the proposed CNN-HHO hybrid model, suggesting its potential as a reliable tool for flood prediction in other regions facing similar hydrological risks.

Keywords

Main Subjects

References

  1. Afsari, R., Nadizadeh Shorabeh, S., Kouhnavard, M., Homaee, M., & Arsanjani, J. J. (2022). A spatial decision support approach for flood vulnerability analysis in urban areas: A case study of Tehran. ISPRS International Journal of Geo-Information, 11(7), 380.
  2. Al-Kindi, K. M., & Alabri, Z. (2024). Investigating the role of the key conditioning factors in flood susceptibility mapping through machine learning approaches. Earth Systems and Environment, 8(1), 63-81.
  3. Arabameri, A., Seyed Danesh, A., Santosh, M., Cerda, A., Chandra Pal, S., Ghorbanzadeh, O., Roy, P., & Chowdhuri, I. (2022). Flood susceptibility mapping using meta-heuristic algorithms. Geomatics, Natural Hazards and Risk, 13(1), 949-974.
  4. Arora, A., Pandey, M., Siddiqui, M. A., Hong, H., & Mishra, V. N. (2021). Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto International, 36(18), 2085-2116.
  5. Bordbar, M., Aghamohammadi, H., Pourghasemi, H. R., & Azizi, Z. (2022). Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques. Scientific Reports, 12(1), 1451.
  6. Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability,8(9), 948.
  7. Costache, R., Țîncu, R., Elkhrachy, I., Pham, Q.B., Popa, M.C., Diaconu, D.C., Avand, M., Costache, I., Arabameri, A., & Bui, D.T. (2020). New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrological Sciences Journal, 65(16), 2816-2837.
  8. Dano, U.L., Balogun, A.L., Matori, A.N., Wan Yusouf, K., Abubakar, I.R., Said Mohamed, M.A., Aina, Y.A., & Pradhan, B. (2019). Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water, 11(3), 615.
  9. Fang, Z., Wang, Y., Peng, L., & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139, 104470.
  10. Fu, S., Lyu, H., Wang, Z., Hao, X., & Zhang, C. (2022). Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility. Journal of Hydrology, 612, 128312.
  11. Ghiasi, V., Ghasemi, S. A. R., & Yousefi, M. (2021). Landslide susceptibility mapping through continuous fuzzification and geometric average multi-criteria decision-making approaches. Natural Hazards,107(1), 795-808.
  12. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.
  13. Hakim, W. L., Rezaie, F., Nur, A. S., Panahi, M., Khosravi, K., Lee, C. W., & Lee, S. (2022). Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. Journal of environmental management, 305, 114367.
  14. Hashemkhani Zolfani, S., Yazdani, M., & Zavadskas, E. K. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, 22, 7399-7405.
  15. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
  16. Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.
  17. Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Bui, D.T. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744-755.
  18. Khosravi, K., Shahabi, H., Pham, B.T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.B., Gróf, G., Ho, H.L., & Hong, H. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311-323.
  19. Li, Y., Osei, F. B., Hu, T., & Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88, 104307.
  20. Liuzzo, L., Sammartano, V., & Freni, G. (2019). Comparison between different distributed methods for flood susceptibility mapping. Water Resources Management, 33, 3155-3173.
  21. Malik, A., Tikhamarine, Y., Sammen, S. S., Abba, S. I., & Shahid, S. (2021). Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms. Environmental Science and Pollution Research, 28, 39139-39158.
  22. Miraki, S., Zanganeh, S. H., Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., & Pham, B. T. (2019). Mapping groundwater potential using a novel hybrid intelligence approach. Water resources management, 33, 281-302.
  23. Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, 588, 125033.
  24. Paryani, S., Neshat, A., Pourghasemi, H. R., Ntona, M. M., & Kazakis, N. (2022). A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping. Science of The Total Environment, 807, 151055.
  25. Paryani, S., Bordbar, M., Jun, C., Panahi, M., Bateni, S.M., Neale, C.M., Moeini, H., & Lee, S. (2023). Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran. Natural Hazards,116(1), 837-868.
  26. Paul, G. C., Saha, S., & Hembram, T. K. (2019). Application of the GIS-based probabilistic models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi river and their comparison. Remote Sensing in Earth Systems Sciences, 2, 120-146.
  27. Pamučar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8(8), 1342.
  28. Prasad, P., Loveson, V. J., Das, B., & Kotha, M. (2022). Novel ensemble machine learning models in flood susceptibility mapping. Geocarto International,37(16), 4571-4593.
  29. Pham, B.T., Jaafari, A., Van Phong, T., Yen, H.P.H., Tuyen, T.T., Van Luong, V., Nguyen, H.D., Van Le, H., & Foong, L.K. (2021). Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geoscience Frontiers,12(3), 101105.
  30. Ramayanti, S., Nur, A. S., Syifa, M., Panahi, M., Achmad, A. R., Park, S., & Lee, C. W. (2022). Performance comparison of two deep learning models for flood susceptibility map in Beira area, Mozambique. The Egyptian Journal of Remote Sensing and Space Science, 25(4), 1025-1036.
  31. Romero, A., Gatta, C., & Camps-Valls, G. (2015). Unsupervised deep feature extraction for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1349-1362.
  32. Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley interdisciplinary reviews: data mining and knowledge discovery, 8(4), e1249.
  33. Saravanan, S., Abijith, D., Reddy, N. M., Parthasarathy, K. S. S., Janardhanam, N., Sathiyamurthi, S., & Sivakumar, V. (2023). Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India. Urban Climate,49, 101503.
  34. Sarğın, B., Alaboz, P., Karaca, S., & Dengiz, O. (2024). Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems. Computers and Electronics in Agriculture, 227, 109466.
  35. Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S., Pham, B.T., Mansouripour, F., Geertsema, M., Clague, J.J., & Bui, D.T. (2021). Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geoscience Frontiers, 12(3), 101100.
  36. Songchon, C., Wright, G., & Beevers, L. (2021). Quality assessment of crowdsourced social media data for urban flood management. Computers, Environment and Urban Systems,90, 101690.
  37. Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information, 9(12), 720.
  38. Tellman, B., Sullivan, J.A., Kuhn, C., Kettner, A.J., Doyle, C.S., Brakenridge, G.R., Erickson, T.A., & Slayback, D.A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80-86.
  39. Tinh, L. D., Thao, D. T. P., Bui, D. T., & Trong, N. G. (2024). Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data. Earth Science Informatics, 1-16.
  40. Ullah, K., Wang, Y., Fang, Z., Wang, L., & Rahman, M. (2022). Multi-hazard susceptibility mapping based on Convolutional Neural Networks. Geoscience Frontiers, 13(5), 101425.
  41. Vilasan, R. T., & Kapse, V. S. (2022). Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India). Natural Hazards, 112(2), 1767-1793.
  42. Vojtek, M., & Vojteková, J. (2019). Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water, 11(2), 364.
  43. Wang, R., Lu, S., & Li, Q. (2019). Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings. Sustainable Cities and Society, 49, 101623.
  44. Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S., & Costache, R. (2019). Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. Journal of environmental management, 247, 712-729.
  45. Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020). Flood susceptibility mapping using convolutional neural network frameworks. Journal of hydrology, 582, 124482. https://fa.wikipedia.org/wiki/%D8%B3%DB%8C%D9%84_%DB%B1%DB%B3%DB%B6%DB%B6_%D8%AA%D8%AC%D8%B1%DB%8C%D8%B4
  46. Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9, 611-629.
  47. Yariyan, P., Avand, M., Abbaspour, R.A., Torabi Haghighi, A., Costache, R., Ghorbanzadeh, O., Janizadeh, S., & Blaschke, T. (2020). Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk, 11(1), 2282-2314.
  48. Youssef, A. M., Pradhan, B., Dikshit, A., & Mahdi, A. M. (2022). Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt. Geocarto International, 37(26), 11088-11115.
  49. Zhang, H., Nguyen, H., Bui, X. N., Pradhan, B., Asteris, P. G., Costache, R., & Aryal, J. (2022). A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm. Engineering with Computers, 1-14.
  50. Zhang, P., Jia, Y., & Shang, Y. (2022). Research and application of XGBoost in imbalanced data. International Journal of Distributed Sensor Networks, 18(6), 15501329221106935.
Water and Irrigation Management
Volume 15, Issue 2
October 2025
Pages 393-409

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