Geomatics and Environmental Engineering, vol. 17, no. 6

Issue

Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

Date Log

Submitted
Feb 27, 2023
Accepted
Aug 31, 2023
Published
Oct 13, 2023
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia

https://doi.org/10.7494/geom.2023.17.6.129
Rido Dwi Ismanto
National Research and Innovation Agency (BRIN), Research Center for Computing, Cibinong, Indonesia
https://orcid.org/0000-0002-2903-6731
Hana Listi Fitriana
National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia
https://orcid.org/0000-0001-6752-8414
Johanes Manalu
National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia
https://orcid.org/0009-0000-1761-3725
Alvian Aji Purboyo
Indonesia University of Education, Department of Geographic Information Science, Bandung, Indonesia
https://orcid.org/0009-0009-3870-2327
Indah Prasasti
National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia
https://orcid.org/0000-0001-5332-6061

Corresponding Author(s) : Rido Dwi Ismanto

rido001@brin.go.id

Geomatics and Environmental Engineering, Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

Abstract

Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.

Keywords

flood-susceptibility assessment random forest frequency ratio Sumedang remote sensing
Ismanto, R. D., Fitriana, H. L., Manalu, J., Purboyo, A. A., & Prasasti, I. (2023). Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia. Geomatics and Environmental Engineering, 17(6), 129–157. https://doi.org/10.7494/geom.2023.17.6.129
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Youssef A.M., Pradhan B., Hassan A.M.: Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences, vol. 62(3), 2011, pp. 611–623. https://doi.org/10.1007/s12665-010-0551-1.
  2. Tehrany M.S., Pradhan B., Mansor S., Ahmad N.: Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, vol. 125, 2015, pp. 91–101. https://doi.org/10.1016/j.catena.2014.10.017.
  3. Sutradhar S., Mondal P.: Prioritization of watersheds based on morphometric assessment in relation to flood management: A case study of Ajay river basin, Eastern India. Watershed Ecology and the Environment, vol. 5, 2023, pp. 1–11. https://doi.org/10.1016/j.wsee.2022.11.011.
  4. Tehrany M.S., Pradhan B., Jebur M.N.: Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, vol. 504, 2013, pp. 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034.
  5. Wang Z., Lai C., Chen X., Yang B., Zhao S., Bai X.: Flood hazard risk assessment model based on random forest. Journal of Hydrology, vol. 527, 2015, pp. 1130–1141. https://doi.org/10.1016/j.jhydrol.2015.06.008.
  6. Kia M.B., Pirasteh S., Pradhan B., Mahmud A.R., Sulaiman W.N.A., Moradi A.: An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, vol. 67(1), 2012, pp. 251–264. https://doi.org/10.1007/s12665-011-1504-z.
  7. Hong H., Panahi M., Shirzadi A., Ma T., Liu J., Zhu A.X., Chen W., Kougias I., Kazakis N.: Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, vol. 621, 2018, pp. 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114.
  8. Khosravi K., Nohani E., Maroufinia E., Pourghasemi H.R.: A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decisionmaking technique. Natural Hazards, vol. 83(2), 2016, pp. 947–987. https://doi.org/10.1007/s11069-016-2357-2.
  9. Chen J., Li Q., Wang H., Deng M.: A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: A case study of the Yangtze River Delta, China. International Journal of Environmental Research and Public Health, vol. 17(1), 2020, 49. https://doi.org/10.3390/ijerph17010049.
  10. Sahoo G.B., Schladow S.G., Reuter J.E.: Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. Journal of Hydrology, vol. 378(3–4), 2009, pp. 325–342. https://doi.org/10.1016/j.jhydrol.2009.09.037.
  11. Tehrany M.S., Pradhan B., Jebur M.N.: Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, vol. 512, 2014, pp. 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008.
  12. Li X., Zhang Q., Shao M., Li Y.: A comparison of parameter estimation for distributed hydrological modelling using automatic and manual methods. Advanced Materials Research, vol. 356–360, 2012, pp. 2372–2375. https://doi.org/10.4028/www.scientific.net/AMR.356-360.2372.
  13. Samanta S., Pal D.K., Palsamanta B.: Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, vol. 8(2), 2018, 66. https://doi.org/10.1007/s13201-018-0710-1.
  14. Liu X., Li X., Liu L., He J., Ai B.: An innovative method to classify remote-sensing images using ant colony optimization. IEEE Transactions on Geoscience and Remote Sensing, vol. 46(12), 2008, pp. 4198–4208. https://doi.org/10.1109/TGRS.2008.2001754.
  15. Li X., Yeh A.G.O.: Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, vol. 16(4), 2002, pp. 323–343. https://doi.org/10.1080/13658810210137004.
  16. Ganjirad M., Delavar M.R.: Flood risk mapping using Random Forest and Support Vector Machine. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. X-4/W1-2022, 2023, pp. 201–208. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-201-2023.
  17. Lee S., Kim J.-C., Jung H.-S., Lee M.J., Lee S.: Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk, vol. 8(2), 2017, pp. 1185–1203. https://doi.org/10.1080/19475705.2017.1308971.
  18. Arlisa S.D., Handayani H.H.: Flood vulnerability analysis using random forest method in Gresik Regency, Indonesia. IOP Conference Series: Earth and Environmental Science, vol. 1127(1), 2023, 012023. https://doi.org/10.1088/1755-1315/1127/1/012023.
  19. Esfandiari M., Jabari S., McGrath H., Coleman D.: Flood mapping using random forest and identifying the essential conditioning factors; A case study in Fredericton, New Brunswick, Canada. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-3-2020, 2020, pp. 609–615. https://doi.org/10.5194/isprs-Annals-V-3-2020-609-2020.
  20. Farhadi H., Najafzadeh M.: Flood risk mapping by remote sensing data and random forest technique. Water, vol. 13(21), 2021, 3115. https://doi.org/10.3390/w13213115.
  21. Mobley W., Sebastian A., Blessing R., Highfield W.E., Stearns L., Brody S.D.: Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: A pilot study in southeast Texas. Natural Hazards and Earth System Sciences, vol. 21(2), 2021, pp. 807–822. https://doi.org/10.5194/nhess-21-807-2021.
  22. Catani F., Lagomarsino D., Segoni S., Tofani V.: Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Development of Hazards and Earth System Sciences, vol. 13(11), 2013, pp. 2815–2831. https://doi.org/10.5194/nhess-13-2815-2013.
  23. Biau G., Scornet E.: A random forest guided tour. TEST, vol. 25(2), 2016, pp. 197–227. https://doi.org/10.1007/s11749-016-0481-7.
  24. Sarkar D., Mondal P.: Flood vulnerability mapping using frequency ratio (FR) model: A case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science, vol. 10(1), 2020, 17. https://doi.org/10.1007/s13201-019-1102-x.
  25. Ghosh R., Sutradhar S., Das N., Mondal P.: A comparative evaluation of GIS based flood susceptibility models: A case of Kopai River Basin, Eastern India. Research Square, 2021. https://doi.org/10.21203/rs.3.rs-705204/v1.
  26. Saha S., Sarkar D., Mondal P.: Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: A study of Raiganj Subdivision, Eastern India. Stochastic Environmental Research and Risk Assessment, vol. 36(6), 2022, pp. 1721–1742. https://doi.org/10.1007/s00477-021-02115-9.
  27. Sarkar D., Saha S., Mondal P.: GIS-based frequency ratio and Shannon’s entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. International Journal of Environmental Science and Technology, vol. 19(9), 2022, pp. 8911–8932. https://doi.org/10.1007/s13762-021-03627-1.
  28. Dutta M., Saha S., Saikh N.I., Sarkar D., Mondal P.: Application of bivariate approaches for flood susceptibility mapping: A district level study in Eastern India. HydroResearch, vol. 6, 2023, pp. 108–121. https://doi.org/10.1016/j.hydres.2023.02.004.
  29. Ullah K., Zhang J.: GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLoS ONE, vol. 15(3), 2020, e0229153. https://doi.org/10.1371/journal.pone.0229153.
  30. Hagen E., Shroder J.F., Lu X.X., Teufert J.F.: Reverse engineered flood hazard mapping in Afghanistan: A parsimonious flood map model for developing countries. Quaternary International, vol. 226(1–2), 2010, pp. 82–91. https://doi.org/10.1016/j.quaint.2009.11.021.
  31. Pradhan B., Lee S., Buchroithner M.F.: A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, vol. 34(3), 2010, pp. 216–235. https://doi.org/10.1016/j.compenvurbsys.2009.12.004.
  32. Glenn E.P., Morino K., Nagler P.L., Murray R.S., Pearlstein S., Hultine K.R.: Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. Journal of Arid Environments, vol. 79, 2012, pp. 56–65. https://doi.org/10.1016/j.jaridenv.2011.11.025.
  33. Sørensen R., Zinko U., Seibert J.: On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, vol. 10(1), 2006, pp. 101–112. https://doi.org/10.5194/hess-10-101-2006.
  34. Jahangir M.H., Mousavi Reineh S.M., Abolghasemi M.: Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, vol. 25, 2019, 100215. https://doi.org/10.1016/j.wace.2019.100215.
  35. Haghizadeh A., Siahkamari S., Haghiabi A.H., Rahmati O.: Forecasting floodprone areas using Shannon’s entropy model. Journal of Earth System Science, vol. 126(3), 2017. https://doi.org/10.1007/s12040-017-0819-x.
  36. Thompson A., Clayton J.: The role of geomorphology in flood risk assessment. Proceedings of the Institution of Civil Engineers: Civil Engineering, vol. 150(5), 2002, pp. 25–29. https://doi.org/10.1680/cien.150.5.25.38634.
  37. Gudiyangada Nachappa T., Tavakkoli Piralilou S., Gholamnia K., Ghorbanzadeh O., Rahmati O., Blaschke T.: Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, vol. 590, 2020, 125275. https://doi.org/10.1016/j.jhydrol.2020.125275.
  38. Pourali S.H., Arrowsmith C., Chrisman N., Matkan A.A., Mitchell D.: Topography Wetness Index application in flood-risk-based land use planning. Applied Spatial Analysis and Policy, vol. 9(1), 2016, pp. 39–54. https://doi.org/10.1007/s12061-014-9130-2.
  39. Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., Husak G., Rowland J., Harrison L., Hoell A., Michaelsen J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes. Scientific Data, vol. 2(1), 2015, 150066. https://doi.org/10.1038/sdata.2015.66.
  40. Muñoz P., Orellana-Alvear J., Willems P., Célleri R.: Flash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm. Water (Switzerland), vol. 10(11), 2018, 1519. https://doi.org/10.3390/w10111519.
  41. Breiman L.: Random forests. Machine Learning, vol. 45, 2001, pp. 5–32. https://doi.org/10.1023/A:1010933404324.
  42. North M.A.: A method for implementing a statistically significant number of data classes in the Jenks algorithm. [in:] Sixth International Conference on Fuzzy Systems and Knowledge Discovery: FSKD 2009, Tianjin, China, 14–16 August 2009. Vol. 1, IEEE, Piscataway 2009, pp. 35–38. https://doi.org/10.1109/FSKD.2009.319.
  43. Regmi A.D., Devkota K.C., Yoshida K., Pradhan B., Pourghasemi H.R., Kumamoto T., Akgun A.: Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, vol. 7(2), 2014, pp. 725–742. https://doi.org/10.1007/s12517-012-0807-z.
  44. Bhandari B.P., Dhakal S.: Lithological control on landslide in the Babai Khola Watershed, Siwaliks Zone of Nepal. American Journal of Earth Sciences, vol. 5(3), 2018, pp. 54–64.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 505 Download : 295

Downloads

Download data is not yet available.

 

 

Most read articles by the same author(s)