GIS-Based Flood Risk Zoning Based On Data-Driven Models

Document Type : Research Paper

Authors

1 Department of surveying and Geomatics Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

3 Department of Civil Engineering, University of Birjand, Birjand, Iran.

Abstract

Increasing the occurrence of floods, especially in cities, and the risks to human, financial, and environmental risks due to its, make flood risk zoning of great importance. The purpose of this study is to estimate the flood risk of the Maneh and Samalghan based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present new combination approaches to determine the effective criteria in flood risk zoning (Maneh and Samalghan). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.1757, 0.0461, and 0.0097, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.

Keywords

References

  1. Papaioannou, G., Vasiliades, L., Loukas, A. (2015). Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping. Water Resources Management 29:399–418. https://doi.org/10.1007/s11269-014-0817-6
  2. Lai, C., Shao, Q., Chen, X., et al. (2016). Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology 542:268–280. https://doi.org/10.1016/j.jhydrol.2016.09.003
  3. Hudson, P., Botzen, W.J.W. (2019). Cost–benefit analysis of flood‐zoning policies: A review of current practice. WIREs Water 6:. https://doi.org/10.1002/wat2.1387
  4. Pourghasemi, H.R., Razavi-Termeh, S.V., Kariminejad, N., et al. (2020). An assessment of metaheuristic approaches for flood assessment. Journal of Hydrology 582:124536. https://doi.org/10.1016/j.jhydrol.2019.124536
  5. Rahmati, O., Zeinivand, H., Besharat, M. (2016). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk 7:1000–1017. https://doi.org/10.1080/19475705.2015.1045043
  6. Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., Nasseri, M. (2019). A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of Hydrology 572:17–31. https://doi.org/10.1016/j.jhydrol.2019.02.034
  7. Sadeghi-Pouya, A., Nouri, J., Mansouri, N., Kia-Lashaki, A. (2017). Developing an index model for flood risk assessment in the western coastal region of Mazandaran, Iran. Journal of Hydrology and Hydromechanics 65:134–145. https://doi.org/10.1515/johh-2017-0007
  8. Guevara, J., Zadrozny, B., Buoro, A., et al. (2018). A hybrid data-driven and knowledge-driven methodology for estimating the effect of completion parameters on the cumulative production of horizontal wells. In: Proceedings - SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers (SPE). DOI: 10.2118/191446-ms.
  9. Wang, X., Liu, H. (2019). A Knowledge-and Data-Driven Soft Sensor Based on Deep Learning for Predicting the Deformation of an Air Preheater Rotor. IEEE Access 7:159651–159660. https://doi.org/10.1109/ACCESS.2019.2950661
  10. Elsheikh, R.F.A., Ouerghi, S., Elhag, A.R. (2015). Flood Risk Map Based on GIS, and Multi Criteria Techniques (Case Study Terengganu Malaysia). Journal of Geographic Information System 07:348–357. https://doi.org/10.4236/jgis.2015.74027
  11. Xiao, Y., Yi, S., Tang, Z. (2017). Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Science of the Total Environment 599–600:1034–1046. https://doi.org/10.1016/j.scitotenv.2017.04.218
  12. Al-Juaidi, A.E.M., Nassar, A.M., Al-Juaidi, O.E.M. (2018). Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arabian Journal of Geosciences 11:1–10. https://doi.org/10.1007/s12517-018-4095-0
  13. Ardiansyah, A., Sumunar, D.R.S. (2020). Flood Vulnerability Mapping Using Geographic Information System (GIS) in Gajah Wong Sub Watershed, Yogyakarta County Province. Geosfera Indonesia 5:47. https://doi.org/10.19184/geosi.v5i1.9959
  14. Woznicki, S.A., Baynes, J., Panlasigui, S., et al. (2019). Development of a spatially complete floodplain map of the conterminous United States using random forest. Science of the Total Environment 647:942–953. https://doi.org/10.1016/j.scitotenv.2018.07.353
  15. Khosravi. K., Nohani. E., Maroufinia. E., Pourghasemi, H.R. (2016). 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 decision-making technique. Natural Hazards 83:947–987. https://doi.org/10.1007/s11069-016-2357-2
  16. Jancewicz, K., Migoń, P., Kasprzak, M. (2019). Connectivity patterns in contrasting types of tableland sandstone relief revealed by Topographic Wetness Index. Science of the Total Environment 656:1046–1062. https://doi.org/10.1016/j.scitotenv.2018.11.467
  17. Vojtek, M., Vojteková. J, (2019). Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water (Switzerland) 11:. https://doi.org/10.3390/w11020364
  18. Eini, M., Kaboli, H.S., Rashidian, M., Hedayat, H. (2020). Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction 50:101687. https://doi.org/10.1016/j.ijdrr.2020.101687
  19. Alam, A., Ahmed, B., Sammonds, P. (2020). Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quaternary International. https://doi.org/10.1016/j.quaint.2020.04.047
  20. Saa-Requejo, A., Martin-Sotoca, J.J., Luis Valenciam, J. et al. (2019). Modified Fournier index as a new metric of integrated degradability index
  21. El_Jerjawi, N.S., Abu-Naser, S.S. (2018). Diabetes Prediction Using Artificial Neural Network. International Journal of Advanced Science and Technology 121:55–64. https://doi.org/10.14257/ijast.2018.121.05
  22. Lee, S., Hong, S.M., Jung, H.S. (2018). GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto International 33:847–861. https://doi.org/10.1080/10106049.2017.1303091
  23. Murray, A.T., Xu, J., Baik, J., et al. (2020). Overview of Contributions in Geographical Analysis: Waldo Tobler. In: Geographical Analysis. DOI: 10.1111/gean.12257.
  24. Wu, D. (2020). Spatially and temporally varying relationships between ecological footprint and influencing factors in China’s provinces Using Geographically Weighted Regression (GWR). Journal of Cleaner Production 261: https://doi.org/10.1016/j.jclepro.2020.121089
  25. Fotheringham, A.S., Oshan, T.M. (2016). Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems 18: https://doi.org/10.1007/s10109-016-0239-5
  26. Oshan, T.M., Li, Z., Kang, W., et al. (2019). MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information 8: https://doi.org/10.3390/ijgi8060269
  27. Aghbashlo, M., Tabatabaei, M., Nadian, M.H., et al. (2019). Prognostication of lignocellulosic biomass pyrolysis behavior using ANFIS model tuned by PSO algorithm. Fuel 253: https://doi.org/10.1016/j.fuel.2019.04.169
  28. Abed, K.A., Ahmad, A.A. (2020). The best parameters selection using pso algorithm to solving for ito system by new iterative technique. Indonesian Journal of Electrical Engineering and Computer Science 18: https://doi.org/10.11591/ijeecs.v18.i3.pp1638-1645
  29. Kennedy, J., Eberhart, R.C. (1997). Discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. DOI: 10.1109/icsmc.1997.637339.
  30. Elfil, M., Negida, A. (2019). Sampling methods in clinical research; an educational review. Archives of Academic Emergency Medicine 7:52. https://doi.org/10.22037/emergency.v5i1.15215
  31. Aad, G., Abbott, B., Abdallah, J., et al. (2014). Measurements of spin correlation in top-antitop quark events from proton-proton collisions at √s =7 TeV using the ATLAS detector. Physical Review D - Particles, Fields, Gravitation and Cosmology 90: https://doi.org/10.1103/PhysRevD.90.112016
  32. Paulino, Â., Guimarães, L.N.F., Shiguemori, E.H. (2019). Hybrid adaptive computational intelligence-based multisensor data fusion applied to real-time UAV autonomous navigation. Inteligencia Artificial 22:162–195. https://doi.org/10.4114/intartif.vol22iss63pp162-195
  33. Saeidian, B., Mesgari, M.S., Pradhan, B., Ghodousi, M. (2018). Optimized location-allocation of earthquake relief centers using PSO and ACO, complemented by GIS, clustering, and TOPSIS. ISPRS International Journal of Geo-Information 7: https://doi.org/10.3390/ijgi7080292
  34. Zemestani, A., Soori, H. (2019). Relationship between fatal road traffic injury rates and Human Development Index in Iran. Journal of Injury and Violence Research 11. https://doi.org/10.5249/jivr.v11i2.1435
Journal of Hydraulic Structures
Volume 6, Issue 4 - Serial Number 14
December 2020
Pages 75-98

Files

Share

How to cite

Statistics