Skip to main content

Advertisement

SpringerLink
Log in

Flood Mapping and Damage Assessment using Ensemble Model Approach

  • Research
  • Published:
Sensing and Imaging Aims and scope Submit manuscript

Abstract

Flood is the most frequently occurring and dangerous natural disaster, which leads to loss of human life, economic loss, and agricultural loss. It also has an impact on a variety of services, including health, education, and transportation etc. So, in order to give assistance and conduct rescue operations promptly, flood detection, its mapping, and flood damage assessment are crucial duties. Additionally, they support urban planning, building design, and other future endeavors. This study focuses on generating flood maps using synthetic aperture radar images from the Sentinel-1 (COPERNICUS/S1_GRD) satellite. Further study includes damage assessments in seven different sectors: urban land, agricultural land, forest land, barren land, range land, permanent water bodies, and unknown. This forecasts how much of the land in these 7 areas was affected by flooding. For the aforementioned land use and land cover classifications, the study proposes the best-fitting ensemble model, which is the aggregate of 3 image segmentation models that are Resnet34, InveptionV3, and VGG16. These three models are trained on the DeepGlobe dataset to give a mean Intersection over Union score of 75.84% and an F1 score of 0.76. A further proposed damage assessment technique is validated on a selected study area, i.e., village Vasagade from Kolhapur district of Maharashtra, which was severely affected in the year 2021s flood.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Hammond, M. J., Chen, A. S., Djordjević, S., Butler, D., & Mark, O. (2015). Urban flood impact assessment: A state-of-the-art review. Urban Water Journal, 12(1), 14–29.

    Article  Google Scholar 

  2. Bremond, P., Grelot, F., & Agenais, A. L. (2013). Economic evaluation of flood damage to agriculture-review and analysis of existing methods. Natural Hazards and Earth System Sciences, 13(10), 2493–2512.

    Article  ADS  Google Scholar 

  3. Sholihah, Q., Kuncoro, W., Wahyuni, S., Suwandi, S. P., & Feditasari, E. D. (2020, February). The analysis of the causes of flood disasters and their impacts in the perspective of environmental law. In IOP conference series: earth and environmental science (Vol. 437, No. 1, p. 012056). IOP Publishing.

  4. CRED Crunch 70 - Disasters Year in Review 2022 https://cred.be/sites/default/files/CredCrunch70.pdf.

  5. Jonkman, S. N. (2005). Global perspectives on loss of human life caused by floods. Natural hazards, 34(2), 151–175.

    Article  MathSciNet  Google Scholar 

  6. Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article" Assessment of economic flood damage". Natural Hazards and Earth System Sciences, 10(8), 1697–1724.

    Article  ADS  Google Scholar 

  7. Molinari, D. A. N. I. E. L. A., Ballio, F. R. A. N. C. E. S. C. O., Handmer, J., & Menoni, S. C. I. R. A. (2014). On the modeling of significance for flood damage assessment. International Journal of Disaster Risk Reduction, 10, 381–391.

    Article  Google Scholar 

  8. Joycee, C. D., & Santhi, M. H. (2015). Assessment of surface runoff from sub basin of Kodayar using NRCS-CN model with GIS. Indian Journal of Science and Technology, 8(13), 1–8.

    Google Scholar 

  9. Romascanu, A., Ker, H., Sieber, R., Greenidge, S., Lumley, S., Bush, D., & Brunila, M. (2020). Using deep learning and social network analysis to understand and manage extreme flooding. Journal of Contingencies and Crisis Management, 28(3), 251–261.

  10. 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.

    Article  Google Scholar 

  11. Luo, X., Tong, X., & Pan, H. (2020). Integrating multiresolution and multitemporal Sentinel-2 imagery for land-cover mapping in the Xiongan New Area, China. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1029–1040.

    Article  ADS  Google Scholar 

  12. Dobrinić, D., Medak, D., & Gašparović, M. (2020). Integration of multitemporal Sentinel-1 and Sentinel-2 imagery for land-cover classification using machine learning methods. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 91–98.

    Article  Google Scholar 

  13. Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.

    Article  ADS  Google Scholar 

  14. Haq, M., Akhtar, M., Muhammad, S., Paras, S., & Rahmatullah, J. (2012). Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 15(2), 135–141.

    Article  Google Scholar 

  15. Tarpanelli, A., Mondini, A. C., & Camici, S. (2022). Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe. Natural Hazards and Earth System Sciences, 22(8), 2473–2489.

    Article  ADS  Google Scholar 

  16. Markert, K. N., Chishtie, F., Anderson, E. R., Saah, D., & Griffin, R. E. (2018). On the merging of optical and SAR satellite imagery for surface water mapping applications. Results in Physics, 9, 275–277.

    Article  ADS  Google Scholar 

  17. Panhalkar, S. S., & Jarag, A. P. (2017). Flood risk assessment of Panchganga River (Kolhapur district, Maharashtra) using GIS-based multicriteria decision technique. Current Science, 785-793.

  18. Smith, D. I. (1981). Actual and potential flood damage: a case study for urban Lismore, NSW. Australia. Applied Geography, 1(1), 31–39.

    Article  Google Scholar 

  19. Penning-Rowsell, E., & Chatterton, J. B. (1977). Benefits of flood alleviation. Saxon House.

    Google Scholar 

  20. Bremond, P., Grelot, F., & Agenais, A. L. (2013). Economic evaluation of flood damage to agriculture-review and analysis of existing methods. Natural Hazards and Earth System Sciences, 13(10), 2493–2512.

    Article  ADS  Google Scholar 

  21. Martínez-Gomariz, E., Forero-Ortiz, E., Guerrero-Hidalga, M., Castán, S., & Gómez, M. (2020). Flood depth-damage curves for Spanish Urban areas. Sustainability, 12(7), 2666.

    Article  Google Scholar 

  22. Pistrika, A., Tsakiris, G., & Nalbantis, I. (2014). Flood depth-damage functions for built environment. Environmental Processes, 1, 553–572.

    Article  Google Scholar 

  23. Arun, R., & Premalatha, K. Flood Damage Assessment using Remote Sensing and GIS: The Past And Present,(2020). International Journal of Civil Engineering and Technology (IJCIET), 11, 12.

  24. Puttinaovarat, S., & Horkaew, P. (2020). Internetworking flood disaster mitigation system based on remote sensing and mobile GIS. Geomatics, Natural Hazards and Risk, 11(1), 1886–1911.

    Article  Google Scholar 

  25. Sajjad, A., Lu, J., Chen, X., Chisenga, C., Saleem, N., & Hassan, H. (2020). Operational monitoring and damage assessment of riverine flood-2014 in the lower Chenab plain, Punjab, Pakistan, using remote sensing and GIS techniques. Remote Sensing, 12(4), 714.

    Article  ADS  Google Scholar 

  26. Pastor-Escuredo, D., Torres, Y., Martinez, M., & Zufiria, P. J. (2018). Floods impact dynamics quantified from big data sources. arXiv preprint arXiv:1804.09129.

  27. Glas, H., Van Ackere, S., Deruyter, G., & De Maeyer, P. (2016). FLOOD DAMAGE ASSESSMENT IN A GIS-CASE STUDY FOR ANNOTTO BAY (p. 54). Flood Risk Management and Response: JAMAICA.

    Google Scholar 

  28. Memon, A. A., Muhammad, S., Rahman, S., & Haq, M. (2015). Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 99–106.

    Article  Google Scholar 

  29. Mohan, S., & Giridhar, M. V. S. S. (2022). A brief review of recent developments in the integration of deep learning with GIS. Geomatics and Environmental Engineering, 16(2).

  30. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5), 823–870.

    Article  ADS  Google Scholar 

  31. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166–177.

    Article  ADS  Google Scholar 

  32. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

  33. Luus, F. P., Salmon, B. P., Van den Bergh, F., & Maharaj, B. T. J. (2015). Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12), 2448–2452.

    Article  ADS  Google Scholar 

  34. Henry, C. J., Storie, C. D., Palaniappan, M., Alhassan, V., Swamy, M., Aleshinloye, D., & Kim, D. (2019). Automated LULC map production using deep neural networks. International Journal of Remote Sensing, 40(11), 4416–4440.

  35. Storie, C. D., & Henry, C. J. (2018, July). Deep learning neural networks for land use land cover mapping. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 3445-3448). IEEE.

  36. Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.

  37. Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.

  38. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing.

  39. Wang, H., Zheng, B., Yoon, S. W., & Ko, H. S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), 687–699.

    Article  MathSciNet  Google Scholar 

  40. Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.

    Article  Google Scholar 

  41. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

  42. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).

  43. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  44. Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing, 13(18), 3778.

    Article  ADS  Google Scholar 

  45. A Q &A Session on Radar Remote Sensing in partnership with Franz Meyer (ASF Chief Scientist/NASA/UAF) https://appliedsciences.nasa.gov/sites/default/files/2020-11/RadarQ

  46. Guan, H., Huang, J., Li, L., Li, X., Miao, S., Su, W., Ma, Y., Niu, Q., & Huang, H. (2023). Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data. Remote Sensing of Environment, 295, 113714.

    Article  Google Scholar 

  47. Li, X., Li, T., Chen, Z., Zhang, K., & Xia, R. (2021). Attentively learning edge distributions for semantic segmentation of remote sensing imagery. Remote Sensing, 14(1), 102.

    Article  ADS  Google Scholar 

  48. Safarov, F., Temurbek, K., Jamoljon, D., Temur, O., Chedjou, J. C., Abdusalomov, A. B., & Cho, Y. I. (2022). Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture. Sensors, 22(24), 9784.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the Department of CSE, COEP Technological University for providing access to the GPU Server facility needed to implement this work. This facility was established under TEQIP-III (a World Bank project). The authors are thankful to the government authority (gram panchayat) and villagers Pradip Rajgonda Patil & Mahavir Dhanpal Patil of Vasagade, Kolhapur, Maharashtra, India, for providing 2021 on-site flood damage assessment details that played an important role in cross-verification.

Funding

The work has not received any funding from any agency.

Author information

Authors and Affiliations

  1. Computer Engineering & IT, COEP Technological University, Wellesley Rd, Shivajinagar, Pune, 411005, Maharashtra, India

    Vrushabh Patil, Yadnyadeep Khadke, Amit Joshi & Suraj Sawant

Authors
  1. Vrushabh Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yadnyadeep Khadke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suraj Sawant
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

VP—Conceptualization, Methodology, Implementation, Writing—Original Draft preparation, writing-review and editing. YK—Writing - Original draft preparation, Methodology, visualization. AJ—Visualization, Supervision. SS—Conceptualization, Visualization, Writing—review and editing.

Corresponding author

Correspondence to Vrushabh Patil.

Ethics declarations

Conflict of interest

The authors do not have conflicts of interest with the any material discussed in this article. The authors have not executed any experiment that causes harm to any unit of nature.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patil, V., Khadke, Y., Joshi, A. et al. Flood Mapping and Damage Assessment using Ensemble Model Approach. Sens Imaging 25, 15 (2024). https://doi.org/10.1007/s11220-024-00464-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11220-024-00464-7

Keywords

Search

Navigation