کاربرد مدل‌های شبکه عصبی مصنوعی، نسبت فراوانی و تابع شواهد قطعی در تهیه نقشه حساسیت به وقوع سیل در حوزه آبخیز هراز: الگویی برای مطالعات مخاطرات سیلاب شهری

نوع مقاله : مقاله پژوهشی

نویسنده

گروه ژئومورفولوژی، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران

10.30495/jupm.2021.4245

چکیده

در این تحقیق برای تهیه نقشه­ حساسیت به وقوع سیل در حوزه آبخیز هراز در استان مازندران از روش­های شبکه عصبی مصنوعی (ANN)، نسبت فراوانی (FR) و تابع شواهد قطعی (EBF) استفاده شده است و برای دستیابی به هدف پژوهش از ده پارامتر­ موثر در وقوع سیلاب از قبیل شیب، انحنای زمین، فاصله از رودخانه، طبقات ارتفاعی، بارش، شاخص توان رودخانه (SPI)، شاخص رطوبت توپوگرافی (TWI)، لیتولوژی، کاربری اراضی و شاخص تفرق پوشش گیاهی (NDVI) استفاده گردید. همچنین، موقعیت جغرافیایی 211 نقطه سیل­گیر در منطقه تهیه شده و نقاط به صورت تصادفی به گروه­هایی متشکل از 151 نقطه (70%) و 60 نقطه (30%) به­ترتیب برای واسنجی و اعتبار­سنجی تقسیم شده­اند. سپس احتمال رخداد سیل برای هر کلاس از هر پارامتر­ محاسبه گردید. وزن­های محاسبه شده برای هر کلاس در سیستم اطلاعات جغرافیایی در لایه­های مربوطه اعمال­گردیده و نقشه­های حساسیت به وقوع سیل منطقه مورد مطالعه به­دست آمد. براساس نقشه پتانسیل سیل­خیزی، منطقه به 5 کلاس با حساسیت خیلی زیاد، زیاد، متوسط، کم و خیلی کم تقسیم گردید. روش­های مذکور توسط روش منحنی مشخصه عملکرد سیستم (AUC) ارزیابی شدند. نتایج حاکی از آن است که طبقات ارتفاعی پایین و نزدیک رودخانه دارای احتمال و حساسیت بالایی نسبت به وقوع سیل می­باشند. همچنین نتایج نشان داد که تکنیک نسبت فراوانی (AUC=0.97)، تابع شواهد قطعی (AUC=0.94) و شبکه عصبی مصنوعی (AUC=0.87) به­ترتیب اولویت، دارای بیشترین دقت در پیش­بینی وقوع سیل بوده­اند. از این­رو مدل­های مذکور به منظور پیش­بینی پتانسیل خطر سیل به­ویژه در نواحی مختلف از جمله فضاهای شهری به دلیل کارایی بالا، می­تواند مفید و قابل اعتماد باشند.

کلیدواژه‌ها


عنوان مقاله [English]

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

نویسنده [English]

  • Himan Shahabi
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
چکیده [English]

In this study, artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) methods were used to prepare the flood susceptibility map. For this purpose, the parameters of ten, slope, land curvature, topographic moisture index, distance from river and geology and type of lands in Haraz watershed in Mazandaran province were performed. Eleven conditioning factors including slope, land curvature, distance to river, river density, elevation, rainfall, stream power index (SPI), topographic wetness index (TWI), lithology, land use and normalized difference vegetation index (NDVI) were used in Haraz watershed in Mazandaran province. In addition, 201 floodplains were located in the area. The points were randomly divided into groups of 141 points (70%) and 60 points (30%) for training and validation, respectively. Furthermore, the probability of flooding for each class of each factor was calculated. Hence, the weights obtained for each class in the Geographic Information System (GIS) were applied in the respective layers, and the flood susceptibility maps of the study area were obtained. Based on the flood susceptibility map, the area was divided into 5 classes with very high, high, medium, low and very low sensitivity. These methods were evaluated by area under the curve (AUC) method. The results indicate that the lower and near elevation to river have a high probability and sensitivity to flooding. The results of the current study showed that the frequency ratio (AUC = 0.97) and evidential belief function (AUC = 0.94) and artificial neural network (AUC = 0.87) methods had the highest accuracy in predicting flood occurrence, respectively. The results suggest that these models can be useful and reliable in predicting flood risk potential, especially in different areas, including urban spaces, due to their high efficiency.
Extended Abstract
Introduction
To prevent, control and control floods and Prevention of possible damages, areas with high flood potential first should be considered and foremost identified and then Identify the factors that produce and create floods. In this regard, the level of flood-prone and flood-prone areas in the country has increased and Many cities, villages, industrial and agricultural facilities and residential areas They are at risk of flooding. In the event of a flood There are many factors involved. Generally Climatic factors, regional factors and human factors play a role in creating floods. Climatic factors can be He pointed to Dry area, heavy rainfall and relatively short continuity. One of the most important factors in the field can be mentioned Geological condition, vegetation, basin area, basin shape and form, basin slope and focal point. Also human intervention in the natural water cycle via Destruction of vegetation in watersheds, Irregular land use, Development of impenetrable levels and the like Increased the likelihood of flooding in various areas. In Sail management, some of these factors are controllable and ‌In design, flood control They need more attention.
Due to the increasing trend of floods in the country and the growing negative effects of its occurrence in the northern parts of the country, its necessary to reduce the risk of loss of life, property and environmental risk, Necessary measures should be considered. among the various watersheds in the north of the country, in this study, Haraz watershed has been selected as the study area That The reason for choosing it on the one hand It is located and adjacent to key cities in the north of the country, including The cities of Amol, Mahmoud Abad, Babol, Babolsar, Ghaemshahr, Sari, Pol-e Sefid, Shirgah, Neka, Behshahr, Galugah and Bandar-e-Gaz and also Hundreds of rural points and thousands of hectares of agricultural and garden lands and Part of the road along the Caspian Sea (Rasht to Gorgan) and Parts of the mountainous roads of Amol to Tehran and Ghaemshahr to Tehran in this basin and on the other hand There has been a growing flood in recent years in this geographical area that Numerous social, economic and environmental damages and challenges. So these are the reasons The preparation of a flood susceptibility map in the Haraz watershed makes it even more necessary. according to the above and Description of flood hazards in the northern regions of the country, the questions in this study are: What are the most dangerous parts of Haraz watershed in terms of flood sensitivity? Efficiency of which of the artificial neural network models, Frequency ratio and Is the function of definitive evidence more to prepare a flood susceptibility map in Haraz watershed?
 
Methodology
Current research in terms of purpose Is a type of applied research and done by quantitative method. According to the objectives of the research, the required data Has been collected from the relevant organizations and organs (Regional Water Company, Natural Resources Department, etc.) and to analyze this data Used the ArcGIS software. Overall, the research process is as follows First Prepared List of past floods in the study area and so on has been identified Effective parameters in flood occurrence and using Three models of definite evidence function (EBF), frequency ratio (FR) and artificial neural network (ANN), A flood sensitization map of Haraz watershed has been prepared. The following is a review Model Validation Using the ROC curve.
 
Results and discussion
The weights obtained in each method, for each class of each factor Applied in Geographic Information System (GIS) and Flood susceptibility maps were prepared for Haraz watershed. Flood susceptibility maps Launched in ArcGIS10.3 software environment in five classes, the sensitivity is very low, low, medium, high and very high. In order to assess the accuracy of the flood prediction map, 60 flood events were used (Experimental data) Related to previous courses and These events have not been entered to predict flood potential in probabilistic models. Given that the area below the curve for the model, the frequency ratio is 0.97 So this model is more efficient Definitive Evidence for Model Function Models (0.93) and The neural network is artificial (0.78).
 
Conclusion
The present study is done with the aim of preparing a map of the possibility of floods in the watershed of Haraz and Evaluate the efficiency of frequency-ratio models, the function of definitive evidence, and the artificial neural network in the preparation of flood susceptibility maps. To do this, 201 flood points were recorded and 141 Flood situation for modeling and 60 positions were set aside for model validation. To prepare these maps, the first step is to prepare the factors that affect the occurrence of floods. The findings of this study indicate the accuracy of the probability frequency ratio model in identifying areas with flood susceptibility in Haraz watershed in Mazandaran province. Therefore, the use of probability frequency model It is useful and reliable in assessing the risk of flooding. But since The accuracy of predicting models of definite evidence and artificial neural networks is also acceptable. These methods can also be used, but in general, the frequency ratio has a higher accuracy in predicting flood areas. In the maps produced, Parts with low and low elevation classes Exit area, they have the highest amount of tracking. generally, Areas with low elevation and low slope, they are most likely to be flooded. The predictive results also showed that Slope parameters, height, land curvature, lithology, land type, river distance, river carrying capacity and topographic moisture index are influential on Potential flooding potential and using them is useful in probabilistic models, flood potential assessment.
Flood formation mechanism and landslide flooding in the form of spatial analysis, it can be extended to other parts of the watershed. The approach presented in this research in fact, some variables affecting the occurrence of floods have been used Which are very important in the flood risk prediction map in the study area which can be used using the results of these maps, He took appropriate management measures to reduce the damage and casualties caused by the floods. To be careful in predicting flood occurrence It is necessary to use other machine learning models or a combination of these models Which will increase the accuracy of the flood prediction. The above findings, in addition to having practical and operational aspects for management devices and institutions in particular, the Crisis Management Headquarters of the northern provinces of the country, can be used as a suitable template, By researchers and those interested in flood urban crisis management planning. Prepare a hybrid susceptibility map for multiple hazards (Flood, earthquake, drought, etc.) Using hybrid models for the study area and other watersheds of the country Especially in areas with high urban population density. Recommended as a basis for future studies.

کلیدواژه‌ها [English]

  • Data mining
  • Artificial neural network
  • Evidential belief function
  • frequency ratio
  • Urban flood risk
  1. 1.      Althuwaynee, O.F., Pradhan, B., and Lee, S. (2012): Application of an evidential belief function model in landslide susceptibility mapping. Comput. Geosci. 44, 120–135.
  2. 2.      Althuwaynee, O.F., Pradhan, B., Park, H.J., and Lee, J.H. (2014): A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process    and    multivariate    statistical    logistic    regression    for    landslide susceptibility mapping. Catena 114, 21–36.
  3. 3.      Billa L, Shattri M, Mahmud AR, and Ghazali, AH. (2006): Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prev Manage 15:233–240.
  4. 4.      Dai, FC/, Lee, CF., Li, J., and Xu, ZW. (2001): Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391.
  5. 5.      Dixon, B. (2005): Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability:  a GIS-based sensitivity analysis. J Hydrol 309:17–38.
  6. 6.      Esfandiary Darabad, F., Rahimi, M., and Pourmortaza, G. (2019): Flood zonation of Agerloo Cay Basin using the L-THIA method and fuzzy logic, quantitative geomorphological researches, 8(2), pp: 155 - 171. (in Persian)
  7. 7.      Ferna´ndez, DS., and Lutz, MA. (2010): Urban flood hazard zoning in Tucuma´n Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98.
  8. 8.      Islam, M.M., Sado, K., Owe, M., Brubaker, K., Ritchie, J., and Rango, A. (2001): Flood damage and management modelling using satellite remote sensing data with GIS: case study of Bangladesh. IAHS Publication, pp. 455-457.
  9. 9.      Jamini, Davood., Amini, Abbas., Ghadermarzi, Hamed and Tavakoli, Jafar  (2017): Measurement of Food Security and Investigation of its Challenges in Rural Areas (Case Study: Badr District from Ravansar County), JOURNAL OF REGIONAL PLANNING, 7 (27): pp: 87-102. (in Persian)
  10. 10.  Jung, IW., Chang, H., and Moradkhani, H. (2011): Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects. Hydrol Earth Syst Sci 15(2):617–633.
  11. 11.  Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., and Bui, D.T. (2018): A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, pp.744-755.
  12. 12.  Khosravi, K., Pourghasemi, H.R., Chapi, K., and Bahri, M. (2016): Environ Monit Assess, 188: 656. doi:10.1007/s10661-016-5665-9.
  13. 13.  Khosroshahi, M. (2016): An overview to identification and prioritization of flood prone areas using SSSE method in sub-watersheds, Iranian Journal of Watershed Management Science and Engineering, 10(33), pp: 59 – 72. (in Persian)
  14. 14.  Kia, MB., Pirasteh, S., Pradhan, B., Mahmud, AR., Sulaiman, WNA., and Moradi, A. (2012): An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264.
  15. 15.  Kron, W. (2002): Keynote lecture: flood risk = hazard * exposure * vulnerability. In: Proceedings of the flood defence. Science Press, New York.
  16. 16.  Lee, M.J., Kang, J.E., and Jeon, S (2012): Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: Geoscience and Remote Sensing Symposium (IGARSS), Munich. 895–898.
  17. 17.  Merz, B., Thieken, AH., and Gocht, M. (2007): Flood risk mapping at the local scale: concepts and challenges. In: Flood risk management in Europe. Springer, Amsterdam, pp 231–251.
  18. 18.  Miller, JR., Ritter DF., and Kochel RC (1990): Morphometric assessment of lithologic controls on drainage basin evolution in the Crawford Upland, south-central Indiana. Am J Sci 290:569–599.
  19. 19.  Moore, ID., Grayson, RB., and Ladson, AR. (1991): Digital terrain modelling: a review of hydrological, geomor- phological, and biological applications. Hydrol Process 5:3–30.
  20. 20.  Nampak, H., Pradhan, B. and Manap, M.A. (2014): Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, pp.283-300.
  21. 21.  Nasiri, Z., and Talebi, A. (2020): prioritization of sub-watersheds from flooding viewpoint using the Hec-Hms model in upstream of Shiraz Khoshk River, Iranian Journal of Eco Hydrology, 7(1), pp: 47-57. (in Persian)
  22. 22.  Nazaripouya, H. (2019): Evaluation of Factor Analysis Method in Prioritizing Flood in Northern Sub-basins of Alvand Hamedan, Journal of Watershed Management Research, 10(20), pp: 49-61. (in Persian)
  23. 23.  Nozari, H., Marofi, S., and Edirsh, M. (2017): Identification and prioritize of potential areas to flood inundation in the Dez basin using WMS, Journal of Range and Watershed Management, 70(3), pp: 805 - 820. (in Persian)
  24. 24.  Oh, H.J., and Pradhan, B. (2011): Application of a neuro-fuzzy model to landslide- susceptibility mapping for shallow landslides in a tropical hilly area. Computer and Geoscience, 37, 1264–1276.
  25. 25.  Ohlmacher, GC., and Davis, JC. (2003): Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343.
  26. 26.  Porhemmat, J. (2016): A model on investigation on flood hazard over watersheds of Iran, Iranian Journal of Watershed Management Science and Engineering, 10 (34), pp: 1-14. (in Persian)
  27. 27.  Pourghasemi, H.R., and Beheshtirad, M. (2014): Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto International, 30(6): 662-685.
  28. 28.  Pourghasemi, H.R., Mohammadi, M., and Pradhan, B. (2012): Landslide susceptibility mapping using index of entropy and conditional probability models at Safarood Basin, Iran. Catena 97, 71–84, <http://dx.doi.org/10.1016/j.catena.2012.05.005>.
  29. 29.  Pradhan B, and Lee, S. (2010): Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037-1054.
  30. 30.  Pradhan, B. (2013): A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci. 51, 350–365.
  31. 31.  Pradhan, B., Abokharima, M.H., Jebur, M.N., and Tehrany, M.S. (2014): Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural hazards, 73(2), pp.1019-1042.
  32. 32.  Pradhan, B., and Youssef, A.M. (2011): A 100year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. Journal of Flood Risk Management, 4(3), pp.189-202.
  33. 33.  Pradhan, B., Hagemann, U., Shafapour Tehrany, M., and Prechtel, N. (2014): An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Comput. Geosci. 63, 34-43.
  34. 34.  SCWMRI (Soil Conservation and Watershed Management Research Institute). (2015): Atlas of the country's watersheds, Publishers: Agricultural Research Education and Extension Organization, Tehran. (in Persian)
  35. 35.  Shamsoddini, Ali and Nasibi, Sasan (2019): The study of urban furniture layout on the urban area's vitality (case study: the whole area of Shiraz), Journal of Research and Urban Planning, 10 (37), pp: 83-96. (in Persian)
  36. 36.  Shamsodini, A., Jamini, D., and Jamshidi, A. (2016): Measurement and Analyses of Social Stability in Rural Area (Case Study: Javanrood Township). Journal of Rural Research. 7(3), 486-503. (in Persian)
  37. 37.  Smith, K. (2001): Environmental hazards assessing risk and reducing disaster, 3rd edn. Routledge, fetter lane, London.
  38. 38.  Talebi, A., Eslami, Z., and Abbasi, A. (2019): Comparing prioritization from flooding of sub-basins using HEC-HMS model and experimental methods in Eskandari Watershed, Journal of Watershed Engineering and Management, 11(2), pp: 336 - 343. (in Persian)
  39. 39.  Tehrany, M.S., Lee, M.J., Pradhan, B., Jebur, M.N., and Lee, S. (2014b): Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ. Earth sci. 72(10): 4001-4015.
  40. 40.  Tehrany, M.S., Pradhan, B., and 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.
  41. 41.  Tehrany, M.S., Pradhan, B., and Jebur, M.N. (2014a): Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J. Hydrol. 512: 332-343.
  42. 42.  Tehrany, M.S., Pradhan, B., Mansour, Sh., and Ahmad, N. (2015): Flood susceptibility assessment using GIS-based support vector machine model with different kernel types.125:91-101.
  43. 43.  Tunusluoglu, M., Gokceoglu, C., Nefeslioglu, H., and Sonmez, H. (2008): Extraction of potential debris source areas by logistic regression technique:  a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environ Geol 54:9–22.
  44. 44.  Youssef, A.M., Pradhan, B., Pourghasemi, H.R., and Abdullahi, S. (2014): Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS. Geosciences Journal, 19(1):113-134.
  45. 45.  Zarghami, S., Teymouri, A., Mohammadian, H and Shamaei, A (2016): Measuring and evaluating urban neighborhood’s resilience against earthquake: the case of Zanjan downtown, Journal of Research and Urban Planning, 7(27): pp: 77-92. (in Persian)