Persian
Language
Geographical Research is Published in Persian Fulltext.
Volume 39, Issue 2 (2024)
GeoRes 2024, 39(2): 161-168 |
Back to browse issues page
Article Type:
Subject:
History
Received: 2024/02/10 | Accepted: 2024/04/17 | Published: 2024/04/28
Received: 2024/02/10 | Accepted: 2024/04/17 | Published: 2024/04/28
How to cite this article
Tavakkoli M, Amirahmadi A, Goli Mokhtari L. Evaluation, Prediction and Regional Analysis of Floods Using Data Mining Models (Frizi Watershed). GeoRes 2024; 39 (2) :161-168
URL: http://georesearch.ir/article-1-1585-en.html
URL: http://georesearch.ir/article-1-1585-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rights and permissions
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
1- Department of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Iran
Abstract (1439 Views)
Aims: Floods are one of the natural hazards that cause financial and human losses every year. This research was carried out to determine the flood risk zones in the Friesian watershed with Artificial Neural Network Model and Support Vector Machine to identify the influential factors in the occurrence of floods in the region, necessary measures for control, planning and protective and management measures in to reduce the risk of flooding.
Methodology: This was an applied, analytical and developmental study carried out in 2023 in Firizi watershed. 14 environmental parameters were used for flood risk zoning. Then, 150 flood points in the basin were extracted using satellite images and randomly divided into 70% for model training and 30% for validation. Then, the layers were weighted, and the flood potential maps were classified into 5 categories: Very high, high, medium, low, and very low, using the natural fracture method. Then, the accuracy of the models used in the research was investigated using the Receiver Operating Characteristic method.
Findings: The research assessed the accuracy of the models by employing the Receiver Operating Characteristic approach. The collected findings indicate that the Artificial Neural Network model outperformed the Support Vector Machine model, with an Area Under the Curve value of 0.923 compared to 0.898.
Conclusion: The flooding in the basin primarily affected areas with gentle slopes, rocks that are resistant to water flow and not easily penetrated, lower altitudes, and lands located next to rivers (known as floodplains). The basin contains areas that are classified as high and extremely high-risk, primarily in its central and outflow regions.
Methodology: This was an applied, analytical and developmental study carried out in 2023 in Firizi watershed. 14 environmental parameters were used for flood risk zoning. Then, 150 flood points in the basin were extracted using satellite images and randomly divided into 70% for model training and 30% for validation. Then, the layers were weighted, and the flood potential maps were classified into 5 categories: Very high, high, medium, low, and very low, using the natural fracture method. Then, the accuracy of the models used in the research was investigated using the Receiver Operating Characteristic method.
Findings: The research assessed the accuracy of the models by employing the Receiver Operating Characteristic approach. The collected findings indicate that the Artificial Neural Network model outperformed the Support Vector Machine model, with an Area Under the Curve value of 0.923 compared to 0.898.
Conclusion: The flooding in the basin primarily affected areas with gentle slopes, rocks that are resistant to water flow and not easily penetrated, lower altitudes, and lands located next to rivers (known as floodplains). The basin contains areas that are classified as high and extremely high-risk, primarily in its central and outflow regions.
Keywords:
References
1. Ahmad M, Al Mehedi MA, Yazdan MMS, Kumar R (2022). Development of machine learning flood model using artificial neural network (ANN) at Var River. Liquids. 2(3):147-160. [Link] [DOI:10.3390/liquids2030010]
2. Al-Areeq AM, Abba SI, Yassin MA, Benaafi M, Ghaleb M, Aljundi IH (2022). Computational machine learning approach for flood susceptibility assessment integrated with remote sensing and GIS techniques from Jeddah, Saudi Arabia. Remote Sensing. 14(21):5515. [Link] [DOI:10.3390/rs14215515]
3. Arab Ameri A, Pourghasemi HR, Shirani K (2017). Flood susceptibility zonation using new ensemble Bayesian-AHP methods (case study: Neka Watershed, Mazandaran Province). Iranian Journal of Ecohydrology. 4(2):447-462. [Persian] [Link]
4. Aristizábal E, Carmona MIA, Gómez FJ, Castro SML, Severiche ADV, Riaño F (2020). Hazard analysis of hydrometeorological concatenated processes in the Colombian Andes. In: Advances in natural hazards and hydrological risks: Meeting the challenge. Berlin: Springer. p. 7-10. [Link] [DOI:10.1007/978-3-030-34397-2_2]
5. Bakhtiar M, Jahantab Z (2022). Spatial modeling of floods using Artificial Neural Network (ANN) and analyst functions of GIS. Journal of Climate Research. 13(49):177-194. [Persian] [Link]
6. Beheshti Rad M, Ahmadi H, Feizniya S, Salajegheh A (2009). Investigating applicability of certainty factor landslide hazard zonation model (a case study Moalemkalayeh watershed). Journal of Physical Geography. 2(5):19-28. [Persian] [Link]
7. Berghuijs WR, Harrigan S, Molnar P, Slater LJ, Kirchner JW (2019). The relative importance of different flood‐generating mechanisms across Europe. Water Resources Research. 55(6):4582-4593. [Link] [DOI:10.1029/2019WR024841]
8. Binh PT, Zhu X, Groeneveld RA, Van Ierland EC (2020). Risk communication, women's participation and flood mitigation in Vietnam: An experimental study. Land Use Policy. 95:104436. [Link] [DOI:10.1016/j.landusepol.2019.104436]
9. Bui DT, Tsangaratos P, Ngo PT, Pham TD, Pham BT (2019). Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Science of the Total Environment. 668:1038-1054. [Link] [DOI:10.1016/j.scitotenv.2019.02.422]
10. Calea GB (2002). Influnce of vegetion cover on flood hydrology in experimental basins if mt. lozere. Hydrology Continental. 7(1):33-49. [Link]
11. Ceola S, Domeneghetti A, Schumann GJP (2022). Unraveling long-term flood risk dynamics across the murray-darling basin using a large-scale hydraulic model and satellite data. Frontiers Water. 3:797259. [Link] [DOI:10.3389/frwa.2021.797259]
12. Chen Y, Chen W, Janizadeh S, Bhunia GS, Bera A, Pham QB, et al (2021). Deep learning and boosting framework for piping erosion susceptibility modeling: Spatial evaluation of agricultural areas in the semiarid region. Geocarto International. 37(16):4628-4654. [Link] [DOI:10.1080/10106049.2021.1892212]
13. Chowdhuri I, Pal SC, Chakrabortty R (2020). Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Advances in Space Research. 65(5):1466-1489. [Link] [DOI:10.1016/j.asr.2019.12.003]
14. Han D, Chan L, Zhu N (2007). Flood forecasting using support vector machines. Journal of Hydroinformatics. 9(4):267-276. [Link] [DOI:10.2166/hydro.2007.027]
15. Hatami Nejad H, Atashafrooz N, Arvin M (2017). Flood hazard zonation using multi-criteria analysis and GIS (case study: Izeh Township). Disaster Prevention and Management Knowledge. 7(2):44-57. [Persian] [Link]
16. Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, et al (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment. 621:1124-1141. [Link] [DOI:10.1016/j.scitotenv.2017.10.114]
17. Kan G, Liang K, Yu H, Sun B, Ding L, Li J, et al (2020). Hybrid machine learning hydrological model for flood forecast purpose. Open Geosciences. 12(1):813-820. [Link] [DOI:10.1515/geo-2020-0166]
18. Khairizadeh M, Maleki J, Amounia H (2013). Flood hazard zoning using ANP model in watershed, case study: Mardaghchay Watershed. Quantitative Geomorphological Research. 1(3):39-56. [Persian] [Link]
19. Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, et al (2019). A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology. 573:311-323. [Link] [DOI:10.1016/j.jhydrol.2019.03.073]
20. Mishra K, Sinha R (2020). Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology. 350:106861. [Link] [DOI:10.1016/j.geomorph.2019.106861]
21. Mollaee Z, Zahiri J, Jalili S, Ansari MR, Taghizadeh A (2018). Estimating suspended sediment concentration using remote sensing and artificial neural network (case study: Karun River). Journal of Water and Soil Science. 22(2):249-259. [Persian] [Link] [DOI:10.29252/jstnar.22.2.249]
22. Mosaffaie J, Kamali M, Pourjam A, Soleymani K, Shahedi K, Gomrokchi A (2020). Evaluation the efficiency of AHP model in prioritizing of Barajin sub watersheds from flood potential viewpoint. Journal of Watershed Management Research. 11(21):48-58. [Persian] [Link] [DOI:10.52547/jwmr.11.21.48]
23. Munawar HS, Hammad AWA, Waller ST (2022). Remote sensing methods for flood prediction: A review. Sensors. 22(3):960. [Link] [DOI:10.3390/s22030960]
24. Nhu VH, Thi Ngo PT, Pham TD, Dou J, Song X, Hoang ND, et al (2020). A new hybrid firefly-PSO optimized random subspace tree intelligence for torrential rainfall induced flash flood susceptible mapping. Remote Sensing. 12(17):2688. [Link] [DOI:10.3390/rs12172688]
25. Omidvar K, Kianfar A, Asgari Sh (2010). Zoning the flood-producing potentials of Konjancham Basin. Natural Geography Researches. 72:73-90. [Link]
26. Peng L, Ruiqing N, Xueling W, Zhao Y, Ye R (2014). Landslide susceptibility mapping based on Rough Set Theory and Support Vector Machines: A case of the Three Gorges Area, China. Geomorphology. 204:287-301. [Link] [DOI:10.1016/j.geomorph.2013.08.013]
27. Peredo D, Ramos MH, Andréassian V, Oudin L (2022). Investigating hydrological model versatility to simulate extreme flood events. Hydrological Sciences Journal. 67(4):628-645. [Link] [DOI:10.1080/02626667.2022.2030864]
28. Pinos J, Quesada-Román A (2022). Flood risk-related research trends in Latin America and the Caribbean. Water. 14(1):10. [Link] [DOI:10.3390/w14010010]
29. Prăvălie R, Costache R (2013). The vulnerability of the territorial-administrative units to the hydrological phenomena of risk (flash-floods). Case study: The subcarpathian sector of Buzău catchment. Annals of the University of Oradea, Geography Series. 23(1):91-98. [Link]
30. Salvati A, Moghaddam Nia A, Salajegheh A, Ghaderi K, Talebpour Asl D, Al-Ansari N, et al (2023). Flood susceptibility mapping using support vector regression and hyper-parameter optimization. Journal of Flood Risk Management. 16(4):e12920. [Link] [DOI:10.1111/jfr3.12920]
31. Sarigöl M, Nur Yesilyurt S (2022). Flood routing calculation with ANN, SVM, GPR, and RTE methods. Polish Journal of Environmental Studies. 31(6):5221-5228. [Link] [DOI:10.15244/pjoes/151542]
32. Sarkar D, Mondal P (2020). Flood vulnerability mapping using frequency ratio (FR) model: A case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science. 10(1):3. [Link] [DOI:10.1007/s13201-019-1102-x]
33. Servati MR, Rostami A, Khodadadi F (2011). Feasibility of flooding in the watershed Leilan chai (Maragheh) CN metod. Quarterly Geographical Journal of Territory. 8(32):67-81. [Persian] [Link]
34. Shahabi H (2021). Application of artificial neural network, frequency ratio, and evidential belief function models in preparing of flood susceptibility map in Haraz watershed: A plan for urban flood risk studies. Research and Urban Planning. 12(45):181-202. [Persian] [Link]
35. Zanganeh Asadi MA, Amir Ahmadi A, Naemitabar M (2021). Efficiency evaluation of the VIKOR, L-THIA, and Artificial Neural Network (ANN) models in flood zone analysis (case study: Khorasan Razavi Province). Iranian Journal of Ecohydrology. 8(1):89-108. [Persian] [Link]