Using citizen reports for urban flood prediction with machine learning approach and rainfall data (Case Study: Qom, Iran)

Document Type : Research Paper

Authors

1 Kish International Campus, University of Tehran, Kish, Iran.

2 Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran.

3 Department of Water Engineering, Faculty of Agricultural Technology, University College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran.

10.22059/jwim.2025.396294.1235

Abstract

This research aims to develop a scientific and practical method for urban flood prediction using citizen reports and satellite rainfall data. The city area is divided into a grid with 3,000-meter intervals, and the intersection points of the grid lines are selected as rainfall measurement stations. Days are classified as wet, dry, and normal based on citizen reports and observed rainfall, and machine learning models including Support Vector Machine, Logistic Regression, and Random Forest are trained on these data. The results show that the Random Forest model has the highest performance, with 98percent precision and an F1 score of 66 percent in correctly identifying flood events. Considering Qom’s semi-arid climate and the non-lethal damages caused by floods, emphasis was placed on the precision of models in correctly detecting flood cases to enhance confidence and prevent community anxiety due to false alarms. Stations 1 and 23 are identified as critical locations for rainfall equipment installation based on feature importance analysis. Subsequently, a critical rainfall threshold curve at a 90 percent probability level was plotted for flood prediction, and validated with real data. Rainfall above the threshold generally results in floods, while values below are considered safe. This approach is an effective tool for rapid warning systems and can aid urban disaster management to prevent financial and human losses.

Keywords

Main Subjects

References

  1. Barnes, L. R., Gruntfest, E. C., Hayden, M. H., Schultz, D. M., & Benight, C. (2007). False alarms and close calls: A conceptual model of warning accuracy. Weather and Forecasting, 22(5), 1140-1147.
  2. Beecham, S., & Chowdhury, R. (2012, May). Effects of changing rainfall patterns on WSUD in Australia. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 165, No. 5, pp. 285-298). Thomas Telford Ltd.
  3. Berkhahn, S., Fuchs, L., & Neuweiler, I. (2019). An ensemble neural network model for real-time prediction of urban floods. Journal of hydrology, 575, 743-754.
  4. Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4). Springer.
  5. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10(7), 1895-1923.
  6. Feng, Q., Liu, J., & Gong, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier-A case of Yuyao, China. Water, 7(4), 1437-1455.
  7. Ghosh, B., Garg, S., & Motagh, M. (2022). Automatic flood detection from sentinel-1 data using deep learning architectures. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 201-208.
  8. Golabi, M. R., Radmanesh, F., Akhoond-Ali, A. M., Niksokhan, M. H., & Kisi, O. (2020). Development of an indirect method for modelling the water footprint of electricity using wavelet transform coupled with the random forest model. Hydrological Sciences Journal, 65(15), 2521-2534.
  9. Hassani, M. R., Niksokhan, M. H., Janbehsarayi, S. F. , & Nikoo, M. R. (2024). Integrated nonurban-urban flood management using multi-objective optimization of LIDs and detention dams based on game theory approach. Journal of Cleaner Production, 142737.
  10. Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-label confusion matrix. Ieee Access, 10, 19083-19095.
  11. Hill, B., Liang, Q., Bosher, L., Chen, H., & Nicholson, A. (2023). A systematic review of natural flood management modelling: Approaches, limitations, and potential solutions. Journal of Flood Risk Management, 16(3), e12899.
  12. Ke, Q., Tian, X., Bricker, J., Tian, Z., Guan, G., Cai, H., Huang, X., Yang, H., & Liu, J. (2020). Urban pluvial flooding prediction by machine learning approaches–a case study of Shenzhen city, China. Advances in Water Resources, 145, 103719.
  13. Kim, H. I., Han, K. Y., & Lee, J. Y. (2020). Prediction of urban flood extent by LSTM model and logistic regression. KSCE Journal of Civil and Environmental Engineering Research, 40(3), 273-283.
  14. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai.
  15. Lee, S., Kim, J.-C., Jung, H.-S., Lee, M. J., & Lee, S. (2017). Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk, 8(2), 1185-1203.
  16. Liu, Y., Zhang, X., Liu, J., Wang, Y., Jia, H., & Tao, S. (2025). A flood resilience assessment method of green-grey-blue coupled urban drainage system considering backwater effects. Ecological Indicators, 170, 113032.
  17. Martina, M., Todini, E., & Libralon, A. (2006). A Bayesian decision approach to rainfall thresholds based flood warning. Hydrology and earth system sciences, 10(3), 413-426.
  18. Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, model tuning, and evaluation of prediction performance. In Multivariate statistical machine learning methods for genomic prediction (pp. 109-139). Springer.
  19. Nikolopoulos, E. I., Crema, S., Marchi, L., Marra, F., Guzzetti, , & Borga, M. (2014). Impact of uncertainty in rainfall estimation on the identification of rainfall thresholds for debris flow occurrence. Geomorphology, 221, 286-297.
  20. O’Shea, D., Nathan, R., Wasko, C., Ho, M., & Sharma, A. (2024). Evaluation of key flood risk drivers under climate change using a bottom-up approach. Journal of Hydrology, 640, 131694.
  21. Panahi, D. M., Destouni, G., Kalantari, Z., & Zahabiyoun, B. (2022). Distinction of driver contributions to wetland decline and their associated basin hydrology around Iran. Journal of Hydrology: Regional Studies, 42, 101126.
  22. Priyambodoho, B. A., Kure, S., Yagi, R., & Januriyadi, N. F. (2021). Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia. Progress in Earth and Planetary Science, 8(1), 34.
  23. Rifath, A. R., Muktadir, M. G., Hasan, M., & Islam, M. A. (2024). Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios. Environmental Challenges, 17, 101029.
  24. Safaei-Moghadam, A., Hosseinzadeh, A., & Minsker, B. (2024). Predicting real-time roadway pluvial flood risk: A hybrid machine learning approach coupling a graph-based flood spreading model, historical vulnerabilities, and Waze data. Journal of Hydrology, 131406.
  25. Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., & Laber, J. L. (2013). Objective definition of rainfall intensity–duration thresholds for the initiation of post-fire debris flows in southern California. Landslides, 10, 547-562.
  26. Tang, X., Machimura, T., Li, J., Yu, H., & Liu, W. (2022). Evaluating seasonal wildfire susceptibility and wildfire threats to local ecosystems in the largest forested area of China. Earth's Future, 10(5), e2021EF002199.
  27. Tanim, H., McRae, C. B., Tavakol-Davani, H., & Goharian, E. (2022). Flood detection in urban areas using satellite imagery and machine learning. Water, 14(7), 1140.
  28. Tella, A., Mustafa, M. R. U., Balogun, A. O., Okolie, C. J., Bello Yamusa, I., & Ibrahim, M. (2023). Spatial prediction of flood in Kuala Lumpur City of Malaysia using logistic regression. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 363-369.
  29. Tian, X., Schleiss, M., Bouwens, C., & van de Giesen, N. (2019). Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations. Science of the total environment, 689, 258-268.
  30. Wasko, C., Nathan, R., Stein, L., & O'Shea, D. (2021). Evidence of shorter more extreme rainfalls and increased flood variability under climate change. Journal of Hydrology, 603, 126994.
  31. Yan, J., Jin, J., Chen, F., Yu, G., Yin, H., & Wang, W. (2018). Urban flash flood forecast using support vector machine and numerical simulation. Journal of Hydroinformatics, 20(1), 221-231.
  32. Yang, L., & Cervone, G. (2019). Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Computing, 23(24), 13393-13408.
  33. Yuan, H., Wang, M., Zhang, D., Ikram, R. M. A., Su, J., Zhou, S., Wang, Y., Li, J., & Zhang, Q. (2024). Data-driven urban configuration optimization: An XGBoost-based approach for mitigating flood susceptibility and enhancing economic contribution. Ecological Indicators, 166, 112247. 
Water and Irrigation Management
Volume 15, Issue 3
December 2025
Pages 443-456

Files

Share

How to cite

Statistics