Predicting flood prone areas using advanced machine learning models (Birjand plain)

Document Type : Research Paper

Authors

1 M.Sc. Graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran.

2 M.Sc., Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

3 Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

4 M.Sc. Graduate, Department of Water and Hydraulic Structure, K. N. Toosi University of Technology, Tehran, Iran.

5 Assistant Professor, Department of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

Abstract

Research on flood predicting models is one of the first steps in reducing flood damage and managing future floods in catchments. The aim of this study was to evaluate flood susceptibility in Birjand plain catchment through four machine learning models including support vector machine (SVM), J48 decision tree, random forest (RF) and Adaptive neuro fuzzy inference system (ANFIS). Therefore, in order to implement and validate the mentioned models, a list of flood-prone areas in the study area was prepared (42 flood-prone locations). In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood occurrence in the study area were extracted to be used to predict flood susceptibility map. The results showed that the highest accuracy was related to the RF model (0.845) and the lowest accuracy was related to the SVM model (0.791). In addition, the validation of the results using the ROC curve showed that the most accurate values of flood susceptibility belong to the RF model (AUC = 0.958). The results of this study can be used to manage vulnerable areas and reduce flood damage.

Keywords

References

  1. Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H., & Panahi, M. (2018). Flood susceptibility assessment using integration of Adaptive Network-Based Fuzzy Inference System (ANFIS) and Biogeography-Based Optimization (BBO) and BAT Algorithms (BA). Geocarto International, 34(11), 1252-1272.
  2. Alam, Z., Zhang, C., & Samali, B. (2020). Influence of seismic incident angle on response uncertainty and structural performance of tall asymmetric structure. The Structural Design of Tall and Special Buildings, 29(12), 1750.
  3. Arabameri, A., Rezaei, K., Cerda, A., Conoscenti, C., & Kalantari, Z. (2019). A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660, 443-458.
  4. Arabgol, R., Sartaj, M., & Asghari, K. (2016). Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environmental Modeling & Assessment, 21(1), 71-82.
  5. Azareh, A., Rafiei Sardooi, E., Choubin, B., Barkhori, S., Shahdadi, A., Adamowski, J., & Shamshirband, S. (2019). Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto International, 1-21.
  6. Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Tien Bui, D., Pham, B.T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229-245.
  7. Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., & Ahmad, B.B. (2019). Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology, 575, 864-873.
  8. Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., & Ahmad, B.B. (2020). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree and random forest methods. Science of the Total Environment, 701,134979.
  9. Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis classification and regression trees and support vector machines. Science of the Total Environment, 651(Pt2), 2087-2096.
  10. Dat, T.T., Tri, D.Q., Truong, D.D., & Hoa, N.N. (2019). Application of mike flood model in inundation simulation with the dam-break scenarios: a case study of Dak-Drinh Reservoir in Vietnam. International Journal of Earth Sciences, 12, 60-70.
  11. de Santana, F.B., de Souza, A.M., & Poppi, R.J. (2018). Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 191, 454-462.
  12. Eftekhari, M., Eslaminezhad, S. A., Haji Elyasi, A., & Akbari, M. (2021a). Predicting Groundwater Potential Areas Using Hybrid Artificial Intelligence Methods (Case study: Birjand Plain). Iranian Journal of Soil and Water Research. (In persian).
  13. Eftekhari, M., Eslaminezhad, S. A., Akbari, M., DadrasAjirlou, Y., & Elyasi, A. H. (2021b). Assessment of the Potential of Groundwater Quality Indicators by Geostatistical Methods in Semi-arid Regions. Journal of Chinese Soil and Water Conservation, 52(3), 158-167.
  14. Eftekhari, M., Eslaminezhad, S., Haji Elyasi, A., & Akbari, M. (2021c). Development of DRASTIC model using artificial intelligence on the potential of aquifer contamination in semi-arid regions. Iranian Journal of Ecohydrology, 8(3), 651-665.
  15. Eslaminezhad, S., Eftekhari, M., Mahmoodizadeh, S., Akbari, M., & Haji Elyasi, A. (2021a). Evaluation of Tree-Based Artificial Intelligence Models to Predict Flood Risk using GIS. Iran-Water Resources Research, 17(2), 174-189. (In persian).
  16. Eslaminezhad, S. A., Omarzadeh, D., Eftekhari, M., & Akbari, M. (2021b). Development af a Data-Driven Model to Predict Landslide Sensitive Areas. Geographia Technica, 16(1).
  17. Gao, W., Moayedi, H., & Shahsavar, A. (2019). The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system. Sol Energy, 183, 293-305.
  18. Ghorbanzadeh, O., Blaschke, T., Aryal, J., & Gholaminia, K. (2020). A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. Journal of Spatial Science, 65(3), 401-417.
  19. Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196.
  20. Giang, P.Q., Trang, N.T.M., Anh, T.T.H., & Binh, N.T. (2020). Prediction of economic loss of rice production due to flood inundation under climate change impacts using a modeling approach: A case study in Ha Tinh Province, Vietnam. Climate Change, 6, 52-63.
  21. Hong, H., Tsangaratos, P., Ilia, I., Liu, , Zhu, A.X., & Chen, W. (2018). Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment, 625, 575-588.
  22. Jancewicz, K., MigoĊ„, 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.
  23. Jang, J.S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
  24. Johann, G., & Leismann, M. (2017). How to realise flood risk management plans efficiently in an urban area–the S eseke project. Journal of Flood Risk Management, 10(2), 173-181.
  25. Kalantari, Z., Ferreira, C.S.S., Walsh, R.P.D., Ferreira, A.J.D., & Destouni, G. (2017). Urbanization development under climate change: hydrological responses in a peri-urban Mediterranean catchment. Land Degradation & Development, 28 (7), 2207-2221.
  26. 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
  27. 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-ofevidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947-987.
  28. Kocaman, S., Tavus, B., Nefeslioglu, H.A., Karakas, G., & Gokceoglu, C. (2020). Evaluation of floods and landslides triggered by a meteorological catastrophe (Ordu, Turkey, August 2018) using optical and radar data. Geofluids, 2020, 1-18.
  29. Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Li, L., Huang, C., & Yu, J. (2016). Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: A case study in Bowen Basin, Australia. Stochastic Environmental Research and Risk Assessment, 30(6),1575-1590.
  30. Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W.N.A., & Ramli, M.F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7(2), 711-724.
  31. Markus, M., Angel, J., Byard, G., McConkey, S., Zhang, C., Cai, X., Notaro, M., & Ashfaq, M. (2018). Communicating the impacts of projected climate change on heavy rainfall using a weighted ensemble approach. Journal of Hydrologic Engineering, 23(4), 04018004.
  32. Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102.
  33. Nachappa, T.G., Piralilou, S.T., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., & Blaschke, T. (2021). Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of hydrology, 125275, 590.
  34. Pham, B.T., Tien Bui, D., & Prakash, I. (2017). Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotechnical and Geological Engineering, 35(6), 2597-2611.
  35. Quiroz, C., Mariun, N., Mehrjou, M.R., Izadi, M., Misron, N., & Mohd Radzi, M.A. (2018). Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement, 116, 273-280.
  36. Rahmati, O., Pourghasemi, H.R., & Zeinivand, H. )2016(. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70.
  37. Rahmati, O., & Pourghasemi, H. R. (2017). Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models. Water Resources Management, 31(5), 1473-1487
  38. Razavi Termeh, V., Kornejady, A., Pourghasemi, H.R., & Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 615, 438-451.
  39. Saedi, A., Saghafian, B., & Moazami, S. (2020). Uncertainty of flood forecasts via ensemble precipitation forecasts of seven NWP models for Spring 2019 Golestan Flood. Iran-Water Resources Research, 16(1), 347-359. (In Persian).
  40. Shahid, S., Wang, X.J., Harun, S.B., Shamsudin, S.B., Ismail, T., & Minhans, A. (2016). Climate variability and changes in the major cities of Bangladesh: observations, possible impacts and adaptation. Regional Environmental Change, 16(2), 459-471.
  41. Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., & Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927-941.
  42. Tehrany, M.S., Pradhan, B., & Jebur, M.N. (2013). 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, 504, 69-79.
  43. Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering, 2012, 1-26.
  44. Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.T., Tran, Q.A., & Nguyen, Q.P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330.
  45. 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.
  46. Zeraatkar, Z., & Hassanpour, F., (2016), Simulation of BirjandUrban FloodUsing HEC-RAS and ARC-GIS. Watershed Management Research Journal, 29(3), 41-56. (In persian).
  47. Ziaiian Firouz Abadi, P., Badragh Nejad, A., Sarli, R., & Babaie, M. (2020). Measurement and identification of areas susceptible to flood spreading from the viewpoint of geological formations in Birjand watershed using RS / GIS. researches in Geographical Sciences, 20 (57),1-24. (In persian).
  48. Zhang, C., & Wang, H. (2019). Robustness of the active rotary inertia driver system for structural swing vibration control subjected to multi-type hazard excitations. Applied Sciences, 9(20), 4391.
  49. Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2018). Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment, 615, 1133-1142.

 

Water and Irrigation Management
Volume 11, Issue 4
February 2022
Pages 885-904

Files

Share

How to cite

Statistics