کاربرد الگوریتم‌های طبقه بندی در پهنه بندی خطر فرسایش بادی استان اصفهان

نوع مقاله : مقاله کامل

نویسنده

گروه برنامه‌ریزی روستایی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران

10.22059/jphgr.2024.382042.1007839

چکیده

فرسایش خاک ممکن است به از بین رفتن کیفیت خاک و در نتیجه، کاهش بهره‌وری خاک منجر شود. یکی از راه‌کارهای اساسی برای مهار و کاهش اثرات مخرب فرسایش، شناسایی مناطق مستعد فرسایش در مناطق است. به همین دلیل تعیین مناطق مستعد به فرسایش نقش مهمی در مدیریت فرسایش در منابع طبیعی دارد. تحقیق حاضر درصدد است تا با استفاده از دو روش یادگیری ماشینی جنگل تصادفی و ماشین بردار پشتیبان و 3327 نقطه وقوع فرسایش، مناطق مستعد به فرسایش را در استان اصفهان را تعیین نماید. عوامل محیطی در چهار گروه اصلی شامل عوامل توپوگرافی، عوامل اقلیمی، عوامل زیستی و عوامل انسان‌ساخت تهیه شدند. بررسی شاخص AUC نشان داد که هر دو مدل دارای دقت مناسبی بوده هرچند مدل جنگل تصادفی (AUC = 0.97) دارای دقت بالاتری نسبت به مدل ماشین بردار پشتیبان (AUC = 0.86) بود. بر اساس نتایج مدل ماشین بردار پشتیبان، حدود 6/11 درصد در کلاس پرخطر و حدود 6/68 درصد در کلاس خطر کم فرسایش قرار دارد. همچنین در مدل جنگل تصادفی حدود 1/20 درصد در کلاس پرخطر و حدود 2/49 درصد در کلاس خطر کم قرار دارد. در این زمینه، نتایج به‌دست‌آمده می‌تواند با ارائه گستره‌ای از مناطق استان اصفهان و بهره‌گیری از این دو مدل، به تصمیم گیران و برنامه‌ریزان در تدوین برنامه‌ای مدیریتی و نیز اتخاذ اقداماتی راهبردی در جهت کنترل فرسایش کمک کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The use of classification algorithms in wind erosion risk zoning in Isfahan province

نویسنده [English]

  • Ahmad hajarian
Department of Rural Planning, Faculty of Geographical Sciences and Planning, University of isfahan, Isfahan, Iran
چکیده [English]

ABSTRACT
Urban weathering refers to the processes of loosening, decay, and eventual deterioration of materials used in various urban constructions. This study focuses on assessing the degree of weathering in gravestones from the Joy-e-Horhor and Khold-e-Barin cemeteries in Yazd. A combination of petrographic analyses and longitudinal monitoring of Schmidt hammer rebound values for hundreds of gravestones was employed to achieve this. The findings indicate that in addition to the petrographic characteristics of the stones, such as mineralogical composition, mineral diversity, and rock texture and fabric, local climatic conditions significantly influence the weathering and degradation of these materials. Key processes contributing to the loss of stone durability include temperature fluctuations leading to thermal expansion and contraction, the albedo effect of the stone, wet-dry cycling, and the crystallization and dissolution of secondary minerals like calcite and gypsum. Gravestones made of travertine and marble, characterized by a predominance of calcite minerals and light-colored surfaces, exhibit higher resistance to weathering compared to other lithologies, provided they are not exposed to excessive moisture or frequent washing. In contrast, low-grade metamorphic rocks such as slate and phyllite are the least suitable for gravestones due to their high density of fractures and cleavage planes. Similarly, dark-colored igneous rocks are prone to rapid durability loss, as the differential thermal expansion and contraction of their constituent minerals in response to temperature changes accelerate their weathering processes.
Extended abstract
Introduction
The role of humans in soil erosion is significant directly and indirectly. For this reason, severe damage caused by soil erosion has prompted managers and researchers to make great efforts to predict and spread soil erosion. Iran is one of the countries that has severe soil erosion due to its location in the dry and semi-arid belt. Examining the surface of the areas affected by soil erosion shows that soil erosion has an increasing trend, so that the survey of land area shows an increasing trend in these areas. Isfahan province has the necessary and high potential for soil erosion due to the special conditions of the region, such as topography, slope, lithological condition (presence of formations with low permeability) and climatic conditions (arid and semi-arid). Therefore, since soil erosion is accompanied by numerous damages, it is very important to check the points prone to erosion risk. According to the mentioned points, the phenomenon of erosion can be investigated and studied despite all its complications. One of the management methods of dealing with soil erosion is determining the points prone to erosion. Therefore, the purpose of this research is to evaluate and determine areas prone to erosion using machine learning methods in Isfahan province.
 
Methodology
In this research, the main goal is to determine the areas prone to erosion. For this reason, in the first step, based on past research, as well as experts' opinions and examining the history of erosion areas, the factors affecting the occurrence of erosion were first determined. Among the influential factors, we can mention 4 main criteria (topographic, biological, climatic and man-made factors) and 17 sub-criteria. Each criterion and its sub-criteria are briefly described below. The map related to all environmental factors was created in a raster format with a pixel size of 30 x 30 square meters. After preparing layers of environmental factors, information about erosion points was extracted. In this research, all information related to the occurrence of erosion in the last 20 years was collected from available sources. In this regard, in addition to the information of the departments of natural resources, watershed management and environmental protection, it was used. In total, after removing duplicate points and merging points with a distance of less than 100 meters, the final layer of erosion points was created as a point. This information was divided into two groups of points necessary for training the model (70%) and points necessary for evaluating the results of the model (30%) completely randomly. In total, 2543 points were used to train and build the models and 784 points were used to evaluate the accuracy of the models. The modeling related to two models of random forest and support vector mation was done in R software and "randomForest" and "e1071" packages, respectively. Also, to determine the relative importance of environmental variables in the occurrence of erosion in Isfahan province, the method of relative importance diagram (varImpPlot) was used in the random forest algorithm.
 
Results and discussion
Investigating the relative importance of the layers used in modeling using the random forest algorithm showed that the layers of vegetation and precipitation were the most important factors in the occurrence of erosion in Isfahan province, respectively (Figure 3). Also, the distance layer from the residential areas was the least important factor in the areas with a history of erosion in Isfahan province. According to the results, in the support vector machine model, the low risk class had the largest area of 7345617 (68.6 percent). In the support vector machine model, the second class with a large area was related to the medium risk class, which includes 19.8% of the study area. While in the random forest model, the average class with an area of 3,276,567 thousand hectares, equivalent to 30.6 of the area, has more value than the support vector machine model. Finally, in the support vector machine model, the lowest area was related to the high-risk class, which covered only 11.6% of the area, while in the random forest model, the lowest area was related to the high-risk class with an area equal to 2157652 hectares. (20.1 percent) was. The evaluation of the accuracy of the support vector machine and random forest models based on the area under the graph (AUC) in the ROC curve showed that both models have good accuracy, but the random forest model has a higher accuracy. Based on the results, the random forest model had a level under the graph of 0.97 and the support vector machine model had a level under the graph of 0.86.
 
Conclusion
Modeling of erosion-prone areas can be a key tool for correct and timely management of natural hazards, including soil destruction. Considering that Iran is located in the dry and semi-arid belt, soil erosion is one of its most important crises. Many managers are trying to discover suitable solutions for erosion management that can be used to manage and control erosion in the shortest possible time. Considering the time required for erosion control and resource investment, accurate estimation of the risk of erosion and preparation of erosion distribution maps is the first step in erosion management and risk assessment. The present research was also carried out in order to investigate the efficiency of different machine learning methods in modeling areas prone to erosion in Isfahan province. In the present research, based on the results of modeling, the layers of vegetation and precipitation were the most important factors in the occurrence of erosion in Isfahan province, and the distance from residential areas was the least important factor in areas with a history of erosion in Isfahan province. Based on the conducted studies, temperature, air humidity, and increase in rainfall are among the natural factors that provide the basis for extensive surface erosion in the regions. In general, the accuracy of the used model depends on many factors, which can be attributed to the characteristics of the studied area, such as topography, factors affecting the occurrence of erosion, the accuracy and type of layers of independent variables for modeling the probability of the risk of erosion, the accuracy of points And the recorded ranges of erosion pointed to the continuous occurrence in the past and the type of prediction algorithm used among the factors affecting the overall accuracy of the classification. Considering the increase of erosion rate and increase of crisis in the regions, the results of this research can be used as a model for erosion management in the study area and help managers in planning and actions and providing facilities in high-risk areas and prone to erosion.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.
 

کلیدواژه‌ها [English]

  • Isfahan Province
  • Random Forest
  • Modeling
  • Soil Erosion
  • Support Vector Machine
  1. اصغری سراسکانرود، صیاد؛ فعال نذیری، مهدی و اردشیر پی، علی‌اصغر. (1398). بررسی اثرات کاربری اراضی بر فرسایش خاک با الگوریتم WLC ) مطالعه موردی: حوضه آبخیز آق‌لاقان‌چای). پژوهش‌های فرسایش محیطی، ۹ (۲)، ۵۳-۷۱.
  2. آرخی، صالح، سلمانی، سمیه، عمادالدین، سمیه. (1402). ارزیابی تأثیر تغییرات کاربری اراضی روی فرسایش و رسوب با استفاده از مدل EPM (مطالعه موردی: حوضه کال‌آجی استان گلستان). جغرافیا و مخاطرات محیطی، 12(3)، 273-301. doi: 10.22067/geoeh.2022.74281.1145
  3. تاری پناه، فریده، رنجبرفردوئی، ابوالفضل، ولی، عباسعلی، مکرم، مرضیه. (1402). طبقه‌بندی لندفرم‌ها با استفاده از شاخص موقعیت توپوگرافی و بررسی ریسک واقعی فرسایش آن‌ها در مناطق کوهستانی (مطالعه موردی: حوضة آبخیز خارستان). نشریه سنجش‌ازدور و GIS ایران، 15(2)، 17-36. doi: 10.48308/gisj.2023.102344
  4. خالقی، سمیه، نصرتی، کاظم، عباسپور، رحیم. (1399). برآورد فرسایش خاک و انتقال رسوب در بالادست حوضه آبخیز بادآور لرستان با استفاده از مدل SWAT. پژوهش‌های ژئومورفولوژی کمّی، 9(3)، 186-202. doi: 10.22034/gmpj.2020.122224
  5. خسروی اقدم، کمال، ممتاز، حمیدرضا، اسدزاده، فرخ. (1398). برآورد عامل فرسایش‌پذیری خاک مدل USLE و ارتباط آن با برخی از ویژگی‌های زمین منظر در بخشی از حوضه آبخیز نازلو چای ارومیه. تحقیقات کاربردی خاک، 7(1)، 31-43.
  6. خلیفه، ابراهیم، کاویان‌پور، محمدرضا، پاک‌پرور، مجتبی، متقی، امین‌اله. (1389). کاربرد سامانه اطلاعات جغرافیایی و سنجش‌ازدور در ارزیابی کمی و کیفی فرسایش بادی، مطالعه موردی: دشت شورجستان آباده. مهندسی و مدیریت آبخیز. 2(1)، 44-55
  7. زنگنه اسدی، محمدعلی، ناعمی تبار، مهناز، زندی، رحمان. (1400). بررسی پتانسیل مناطق مستعد فرسایش با مدل‌های ICONA، ماشین بردار پشتیبان، چاید و جنگل تصادفی (مطالعه موردی: حوضه گناباد). جغرافیا و مخاطرات محیطی، 10(4)، 93-112. doi: 10.22067/geoeh.2021.71162.1080
  8. دهمرده قلعه‌نو، محمدرضا، نهتانی، محمد، خالدی، سعیده. (1398). نقش عوامل انسانی بر تشدید فرسایش بادی در منطقه هامون هیرمند. مهندسی و مدیریت آبخیز، 11(3)، 609-618. doi: 10.22092/ijwmse.2018.108923.1250
  9. مزبانی، مهدی، رضایی مقدم، محمدحسین، حجازی، اسد اله. (1400). ارزیابی خطر فرسایش خاک در کاربری‌های اراضی با استفاده از معادله اصلاح‌شده جهانی فرسایش خاک (مطالعه موردی: حوضه آبریز سیکان). جغرافیا و مخاطرات محیطی، 10(1)، 41-63. doi: 10.22067/geoeh.2021.67238.0
  10. مددی، عقیل، اصغری سراسکانرود، صیاد, نگهبان، سعید، مرحمت، مهری. (1402). کاربرد ماشین بردار پشتیبان (SVM) و درخت رگرسیون تقویت‌شده (BRT) جهت مدل‌سازی حساسیت فرسایش خندقی در حوضه آبخیز رودخانه شور (شهرستان مُهر). پژوهش‌های جغرافیای طبیعی، 55(4)، 83-101. doi: 10.22059/jphgr.2023.360424.1007775
  11. معتمدی راد، محمد, زنگنه اسدی، محمدعلی، عجم, حسین. (1402). بررسی میزان فرسایش خاک و تولید رسوب با استفاده از مدل) RUSLE ) و روش پسیاک اصلاح‌شده (مطالعه موردی: حوضه آبریز کال اسماعیل دره شهرستان شاهرود استان سمنان). پژوهش‌های ژئومورفولوژی کمّی، 11(4)، 147-165. doi: 10.22034/gmpj.2022.360813.1374
  12. Asghari saraskanroud S, faal naziri M, ardashirpay A A. (2019). studying the effects of land use on soil Erosion with WLC algorithm. Case of study: Agh Laghan Chay basin. E.E.R. 9, (2), 53-71. [In Persian]
  13. Arekhi, S., Salmani, S., & emadodin, S. (2023). Assessing the Impact of Land Use Changes on Erosion and Sediment Using Remote Sensing and GIS (Case Study: Kala aji Watershed, Golestan Province). Journal of Geography and Environmental Hazards, 12(3),12-19. doi: 10.22067/geoeh.2022.74281.1145
  14. Ashraf, A. (2020). Risk modeling of soil erosion under different land use and rainfall conditions in Soan river basin. Sub -Himalayan region and mitigation options. Modeling Earth Systems and Environment, 6: 417–428. 10.1007/s40808-019-00689-6
  15. Bagio, B., Bertol, I., Wolschick, N.H., Schneiders, D., Santos, M.A.d.N.d., (2017). Water Erosion in Different Slope Lengths on Bare Soil. Revista Brasileira de Ciência do Solo, 41p. https://doi.org/10.1590/18069657rbcs20160132
  16. Christos, G., Panagos, P., & Ioannis, Z. G. (2014). A classification of water erosion models according to their geospatial characteristics. International Journal of Digital Earth, 7, 229- 250. https://doi.org/10.1080/17538947.2012.671380
  17. Dahmardeh Ghaleno, M. R., Nohtani, M., & Khaledi, S. (2019). Effect of anthropogenic factors on wind erosion intensification in Hirmand Hamoon Region. Watershed Engineering and Management11(3), 609-618. doi: 10.22092/ijwmse.2018.108923.1250
  18. Green, C., Diepernk, G., EK, K., Hegger, D., Pettersson, M., Priest, S., Tapsell, S. (2014). Flood risk management in Europe: the flood problem and interventions. Star flood. 1- 250.
  19. Guo, Y., Xion, Y. (2017). Comparison of the implementation of three common types of coupled CFD-DEM model for simulating soil surface erosion. International Journal of Multiphase Flow, 91: 89-100. https://doi.org/10.1016/j.ijmultiphaseflow.2017.01.006
  20. Khalife, E., Kavianpour, M. R., Pakparvar, M., & Mottaghi, A. (2010). Application of geographical information systems and remote sensing in ‌qualitative and quantitative assessment of wind erosion,‌ Case study: Shoorjestan plain. Watershed Engineering and Management, 2(1), 44-55. [In Persian]
  21. Kairis, O., Karavitis, C., Kounalaki, A., Salvati, L., & Kosmas, C. (2013). The effect of landmanagementpractices on soil erosion and land desertification in an olive grove. Soil Use and Management, 29(4), 597-606.
  22. Khaleghi, S., Nosrati, K., & Abbaspour, R. (2020). Estimation Of Soil Erosion And Sediment Transport By SWAT Model (Case Study: Upstream Of Badavar Basin, Lorestan). Quantitative Geomorphological Research, 9(3): 186-202. Doi: 10.22034/Gmpj.2020.122224
  23. Khosraviaqdam, K., Momtaz, H. R., & Asadzadeh, F. (2019). Estimation of Soil erodibility factor of USLE model and its relationship with landscape features in some parts of Nazzlo-Chay basin, Iran. Applied Soil Research, 7(1), 31-4.. [In Persian]
  24. Jahun, B, G. Ibrahim, R. Dlamini, N, S. Musa, S.M. (2015). Review of Soil Erosion Assessment using RUSLE Model and GIS. Journal of Biology. Agriculture and Healthcare, 5(9), 36-47.
  25. Madadi, A., asghari saraskanroud, S., Neghahban, S., & Marhamat, M. (2023). Application of Support Vector Machine (SVM) and Boosted Regression Tree (BRT) to Model the Sensitivity of Gully Erosion in the Watershed of Shore River Moher City. Physical Geography Research55(4), 83-101. doi: 10.22059/jphgr.2023.360424.1007775. [In Persian]
  26. Motamedirad, M., Zangane Asadi, M. A., & Ajam, H. (2023). Investigating the rate of soil erosion and sediment production using the RUSLE model and the modified method PSIAC (case study: kal basin of Ismail, Shahrood city, Semnan province). Quantitative Geomorphological Research, 11(4): 147-165. doi: 10.22034/gmpj.2022.360813.1374
  27. Moayeri, M., & Entezar, M. (2008). Floods And Reviow Floods In Province Of Esfahan. Journal of The Studies Of Human Settlements Planning, 3(6), 109-123. [In Persian]
  28. Mezbani, M., Rezaei Moghadam, M., & Hejazi, A. (2021). Assessment of soil erosion risk in land uses using Revised Universal Soil Loss Equation (Case Study: Sikan Basin). Journal of Geography and Environmental Hazards, 10(1), 41-63. doi: 10.22067/geoeh.2021.67238.0. [In Persian]
  29. Shafaqi.S. (2008) Geography of Isfahan. Isfahan University Publications. [In Persian]
  30. Reis, m., Dutal, H., Bolat, N., Savac, G. (2017). Soil Erosion Risk Assessment Using GIS and ICONA: A Case Study in Kahramanmaras, Turkey. Journal of Agricultural Faculty of Gaziosmanpasa University, 34(1), 64-75.
  31. Poesen, J. (2018). Soil erosion in the Anthropocene: research needs. Earth Surface Processes and Landforms, 43, 64-84. https://doi.org/10.1002/esp.4250
  32. Pimentel, D., Harman, R., Pacenza, M., Pecarsky, J., & Pimentel, M. (1994). Natural resources and an optimum human population. Population and environment15, 347-369. Taripanah, F., Ranjbar, A., Vali, A., & Mokarram, M. (2023). Classification of Landforms Using Topographic Location Index and Assessment of their Actual Soil Erosion Risk in Mountainous Areas (Case Study: Kharestan Watershed). Iranian Journal of Remote Sensing & GIS15(2), 17-36. doi: 10.48308/gisj.2023.102344. [In Persian]
  33. Wainwright, J., Parsons, A. J., & Abrahams, A. D. (2000). Plot‐scale studies of vegetation, overland flow and erosion interactions: Case studies from Arizona and New Mexico. Hydrological Processes14(16‐17), 2921-2943. https://doi.org/10.1002/1099-1085(200011/12)14:16/17<2921::AID-HYP127>3.0.CO;2-7
  34. Wu YeNan, W. Y., Zhong PingAn, Z. P., Zhang Yu, Z. Y., Xu Bin, X. B., Ma Biao, M. B., & Yan Kun, Y. K. (2015). Integrated flood risk assessment and zonation method: a case study in Huaihe River basin. China. Doi:10.1007/s11069-015-1737-3
  35. Zhu, S., Li, D., Huang, G., Chhipi-Shrestha, G., Nahiduzzaman, K.M., Hewage, K., Sadiq, R. (2020). Enhancing urban flood resilience: a holistic framework incorporating historic worst flood to Yangtze River Delta, China. International Journal of Disaster Risk Reduction, 61, 1-52.
  36. Zerihun, M., Mohammedyasin, M.S., Sewnet, D., Adem, A.A., Lakew, M. (2018). Assessment of soil erosion using RUSLE, GIS and remote sensing in NW Ethiopia. Geoderma Reg, 12, 83–90. https://doi.org/10.1016/j.geodrs.2018.01.002
  37. Zhang, W., Zhou, J., Feng, G., Weindorf, D. C., Hu, G., & Sheng, J. (2015). Characteristics of water erosion and conservation practice in arid regions of Central Asia: Xinjiang, China as an example. International Soil and Water Conservation Research3(2), 97-111.
  38. Zangeneh Asadi, M. A., Naemi Tabar, M., & Zandi, R. (2022). Investigating the Potential of Erosion-Prone Areas with ICONA Models, Support Vector Machine, Chaid and Random Forest (Case Study: Gonabad Basin). Journal of Geography and Environmental Hazards, 10(4): 93-112. doi: 10.22067/geoeh.2021.71162.1080. [In Persian]