| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,012 |
| تعداد مقالات | 33,669 |
| تعداد مشاهده مقاله | 44,702,529 |
| تعداد دریافت فایل اصل مقاله | 37,018,004 |
شبیه سازی حساسیت زمین لغزش با مدل های داده‌کاوی در منطقه رابُر، استان کرمان | ||
| پژوهش های آبخیزداری | ||
| مقاله 9، دوره 35، شماره 2 - شماره پیاپی 135، تیر 1401، صفحه 101-118 اصل مقاله (2.18 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.356462.1439 | ||
| نویسنده | ||
| الهام رفیعی ساردویی* | ||
| دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه جیرفت، کرمان، ایران | ||
| چکیده | ||
| زمینلغزش یکی از رویدادهای طبیعی مهم است که هر ساله موجب زیانهای مالی و تخریب اندوخته‌های طبیعی میشود. منطقه‌ی رابُر در سرآب آبخیز هلیل‌رود به‌دلیل بودنِ سازندهای با خاک رُس در آن آماده‌ی زمینلغزش است، و این موجب وارد شدن اندازه‌های زیادی از مواد معلق به سد صفا در منطقه‌ی رابُر شده است. هدف از این پژوهش پهنهبندی این رویداد زیست‌محیطی با مدلهای شبکه‌ی عصبی پیچشی، تابع شاهد قطعی، و ماشین بردار پشتیبان در رابُر است. عاملهای ارتفاع، شیب، فاصله از گسل، زمینشناسی، کاربری زمین، نوع خاک، پوشش گیاهی، فاصله از رود، و بارندگی به‌کار برده‌شد. نقشه‌ی پراکنش زمینلغزشها در جایگاه متغیر وابسته با دادههای سازمان زمینشناسی و بازدیدهای میدانی با بهره‌گیری از جی‌پی‌اس تهیه شد. از 70 زمینلغزش، 49 زمینلغزش (70%) برای شبیهسازی و 21 زمینلغزش (30%) برای اعتبارسنجی مدل به‌کار برده‌شد. نتیجه‌ی اعتبارسنجی مدلها با منحنی ROC نشان داد که اندازه‌های سطح زیر منحنی برای مدلهای شبکه‌ی عصبی پیچشی، ماشین بردار پشتیبان، و تابع شاهد قطعی به ترتیب 0/987، 0/958 و 0/899 است. به طور کلی، نتیجه‌ها هم‌خوانی رضایت‌بخشی میان دادههای زمینلغزش در منطقه و نقشههای حساسیت زمینلغزش نشان داد، و کارکرد مدل یادگیری عمیق شبکه‌ی عصبی پیچشی بیش‌تر از دو مدل دیگر بود. نقشه‌ی حساسیت زمینلغزش در چهار رده با حساسیت کم، متوسط، زیاد و خیلی زیاد رده‌بندی شد. بر پایه‌ی نتیجه‌ی خروجی هر سه مدل بخشهای مرکزی، جنوب شرقی و جنوب غربی منطقه در خطر زیاد و خیلی زیاد زمینلغزش است. انجام طرحهای مناسب مانند دیوارهای حائل، جلوگیری از نفوذ آب، زه‌کشی مناسب، و کاشت پوشش گیاهی متناسب با محیط در دامنههای آماده‌ی لغزش ممکن است برای جلوگیری از این رویداد و مهار آن مناسب باشد. | ||
| کلیدواژه‌ها | ||
| تابع شاهد قطعی؛ سامانه‌ی اطلاعات جغرافیایی؛ شبکه‌ی عصبی پیچشی؛ شبیه سازی؛ ماشین بردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Landslide Susceptibility Simulation Using Data Mining Models in Rabor Area | ||
| نویسندگان [English] | ||
| Elham Rafiei Sardooi | ||
| Associate Professor, Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft | ||
| چکیده [English] | ||
| Landslide, as one of the most important natural hazards, causes financial losses and destruction of natural resources, every year. Rabar area, located upstream of Halilrood watershed, is prone to landslides due to the presence of marl formations and hence, a high amount of sediment has entered the Safa reservoir in Rabar city. Therefore, the purpose of this study is to zone this environmental hazard using convolutional neural network (CNN), support vector machine (SVM) and evidential belief function (EBF) models in Rabor region. To achieve this purpose, the parameters of altitude, slope, and distance from the fault, geology, land use, soil type, Normalized Difference Vegetation Index, and distance from the river were used. Then, using the data of the Geological Survey of Iran and field observations using GPS, a landslide distribution map was prepared as a dependent variable. There were 70 landslides, 49 (70%) of which were used for simulating and 21 (30%) for model validation. The results obtained from the validation of the models using the ROC showed that the AUC values for CNN, SVM and EBF models were 0.987, 0.958 and 0.899, respectively. Overall, the results showed a satisfactory correlation between the landslide data available in the area and the landslide susceptibility maps and the deep learning model of the convolutional neural network had a higher performance compared with the other two models. Finally, the landslide susceptibility map was classified into four classes: low, medium, high, and very high susceptibility. According to the results of all models; the central, southeastern, and southwestern parts of the study area have a high and very high landslide risk. Carrying out appropriate designs such as retaining walls, preventing water infiltration, appropriate drainage, planting vegetation suitable for the environment, and landslide-prone slopes and etc can be appropriate in preventing and controlling this hazard. | ||
| کلیدواژه‌ها [English] | ||
| Convolutional neural network, evidence belief function, geographic information system, simulation, support vector machine | ||
| مراجع | ||
|
Althuwaynee OF, Pradhan B, Lee S. 2012. Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences. 44: 120–135. Amiri M, Solaimani K, Tamartash R, Miryaghoubzadeh M. 2013. Identification of invasive species using remote sensing and vegetation indices (Case study: Vazroud Rangelands, Iran). Journal of Rangeland Science. 3(1): 59–70. Arabameri A, Shirani K, Rezaeei Kh. 2017. A comparative assessment between weights-of-evidence and frequency ratio models for landslide hazard zonation in Vanak Basin. Journal of Watershed Management Research. 8(15): 147–160. (In Persian) Beheshti Z, Gharagozlou A, Monavari M, Zarkesh MK. 2021. Landslides behavior spatial modeling by using evidential belief function model, Promethean II model, and index of entropy in Tabriz, Iran. Arabian Journal of Geosciences. 14(17): 1–22. Bui DT, Hoang ND, Martínez-Álvarez F, Ngo PTT, Hoa PV, Pham TD, ..., Costache R. 2020. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of The Total Environment. 701: 1–38. Carranza EJM, Hale M. 2003. Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geology Reviews. 22(1–2): 117–132. Chen W, Pourghasemi HR, Kornejady A, Zhang N. 2017. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma. 305: 314–327. Chen YC, Chang KT, Lee HY, Chiang SH. 2015. Average landslide erosion rate at the watershed scale in southern Taiwan estimated from magnitude and frequency of rainfall. Geomorphology. 228: 756–764. Emad al-Din S, Babajani H. 2017. Landslide hazard mapping using hierarchical analysis (AHP) in Babolrood catchment, the 1st National Conference on New Ideas and Technologies in Geographical Sciences, University of Zanjan, Zanjan, Iran, 10 p. (In Persian). Fang J, Piao S, He J, Ma W. 2004. Increasing terrestrial vegetation activity in China, 1982–1999. Science in China Series C: Life Sciences. 47(3): 229–240. Gholami M, Soleymani K, Nekoee E. 2017. Landslide susceptibility mapping by use of Weight of Evidence (WofE) and Frequency Ratio (FR) and Dempster-Shafer (DSH) models: A case study of Sari-Kiasar region, Northern Iran. Journal of Rangeland and Watershed Management. 70(3): 735–750. (In Persian). Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, 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. Hofmann T, Schölkopf B, Smola AJ. 2008. Kernel methods in machine learning. The Annals of Statistics. 36 (3): 1171–1220. Hong H, Naghibi SA, Pourghasemi HR, Pradhan B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arabian Journal of Geosciences. 9(2): 1–26. Jebur MN, Pradhan B, Tehrany MS. 2014. Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(2): 674–690. Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S. 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk. 9(1): 49–69. Karami F, Bayati Khatibi M, Kheirizadeh M, Mokhtari Asl A. 2019. Evaluation of performance of support vector machine algorithm in landslide susceptibility zoning in Ahar-chai Basin. Journal of Geographic and Environmental Hazards. 8(4): 1–17. Khan A, Gupta S, Gupta SK. 2020. Multihazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction. 47: 1–34. Kornejady A, Pourghasemi HR. 2019. Landslide susceptibility assessment using data mining models, A case study: Chehel-Chai Basin. Watershed Engineering and Management. 11(1): 28–42. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521 (7553): 436–444. Li R, Wang N. 2019. Landslide susceptibility mapping for the Muchuan County (China): A comparison between bivariate statistical models (woe, ebf, and ioe) and their ensembles with logistic regression. Symmetry. 11(6): 1–24. Mandal K, Saha S, Mandal S. 2021. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geoscience Frontiers. 12(5): 1–17. Mirzania S, Shahabi H. 2020. Evaluation and spatial prediction of landslide hazard in mountainous road of Sanandaj-Kamyaran using advanced data mining algorithms. Environmental Hazards Management. 6(4): 317–340. (In Persian). Mohammadnia M, Fallah G. 2018. Landslide susceptibility mapping using fuzzy logic and AHP. Researches in Geographical Sciences. 18 (48): 115–130. (In Persian). Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer and weights-of- evidence models. Journal of Asian Earth Sciences. 61: 221–236. Mojarad Z, Jamalabadi J, Shafiei N, Zanganeh Asadi M, Parak K. 2020. The zoning of the risk of mass movements using information valuation methods and surface density in the Ghouchan- Shirvan watershed. Journal of Spatial Analysis Environmental Hazards. 7 (3): 147–162. (In Persian). Mondal S, Mandal S. 2020. Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India. Geocarto International. 35(8): 818–856. Moradi HR, Mohammadi M, Pourghasemi HR, Feiznia S. 2010. Landslide risk analysis and estimation using hierarchical analysis process method in a part of Haraz road. The Journal of Spatial Planning. 14(2): 234–247. (In Persian). Naimi B, Araujo MB. 2016. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography. 39(4): 368–375. Nampak H, Pradhan B, Manap MA. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology. 513: 283–300. Neuhäuser B, Terhorst B. 2007. Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology. 86(1–2): 12–24. Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S. 2021. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers. 12(2): 505–519. Nhu VH, Zandi D, Shahabi H, Chapi K, Shirzadi A, Al-Ansari N,..., Nguyen H. 2020. Comparison of support vector machine, Bayesian logistic regression, and alternating decision tree algorithms for shallow landslide susceptibility mapping along a mountainous road in the west of Iran. Applied Sciences. 10(15): 1–27. Oh HJ, Kadavi PR, Lee CW, Lee S. 2018. Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomatics, Natural Hazards and Risk. 9(1): 1053–1070. Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S. 2020. Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology. 588: 1–-12. Pandey R. 2002. Analysis of water resources parameters for model development through application of satellite remote sensing and geographic information system in semi-arid environment. Ph.D. Dissertation. Department of Civil and Building Engineering. Loughborough University. 485 p. Pandey VK, Pourghasemi HR, Sharma MC. (2020). Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto International. 35(2): 168–187. Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Van Phong T, Khoi DK, Shirzadi A, Bui DT. 2020a. A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto International. 35 (12): 1267–1292. Pradhan B, Hagemann U, Tehrany MS, Prechtel N. 2014. An easy-to-use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Computers & geosciences. 63: 34–43. Pradhan B. 2010. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing. 38(2): 301–320. Rahamana SA, Aruchamy S, Jegankumar R. 2014. Geospatial approach on landslide hazard zonation mapping using multicriteria decision analysis: A study on Coonoor and Ooty, part of Kallar watershed, The Nilgiris, Tamil Nadu. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 40(8): 1417–1422. Rahimi M, Solaimani K. 2017. Remote sensing and GIS based assessment groundwater potential zones mapping using multi-criteria decision-making technique. Iran-Watershed Management Science & Engineering. 10(35): 27–38. (In Persian). Roccati A, Paliaga G, Luino F, Faccini F, Turconi L. 2021. GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land. 10(2): 1–28. Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC. 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, Md, 371 p. Salehi M, Safamehr M, Nasri M, Boor H. 2017. Effects of landslides on the safety of roads and rural settlements in Iran and stabilization methods (Case study of Naghan-Karoon 4 road landslides), Housing and Rural Environment. 36(158): 77–88. Sameen MI, Pradhan B, Lee S. 2020. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena. 186: 1–13 Sardooi ER, Azareh A, Mesbahzadeh T, Sardoo FS, Parteli EJ, Pradhan B. 2021a. A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran. Environmental Earth Sciences. 80(15): 1–25. Sardooi ER, Pourghasemi HR, Azareh A, Sardoo FS, Clague, JJ. 2021b. Comparison of statistical and machine learning approaches in land subsidence modelling. Geocarto International. (just-accepted), pp. 1–41. Shahabi H, Khezri S, Ahmad BB, Hashim M. 2014. Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena. 115 (4): 55–70. Shirani K, Arabameri A. 2015. Landslide hazard zonation using logistic regression method (Case study: Dez-e-Oulia Basin), Journal of Water and Soil Science, Isfahan University of Technology. 19(72): 321–334. (In Persian). Silakhori Z, Vahabzadekebriya G, Poorghasemi H. 2021. Landslide hazard assessment using dempster-shafer model (Case study: Part of Talar Watershed). Environmental Erosion Research. 11 (3): 82–98. (In Persian). Soleimani A, Farrokhnia A, Motamedi H. 2017. Landslide susceptibility zoning in the area of drilling site of well A using Analytic Hierarchy Process (AHP). Journal of Exploration & Production Oil & Gas. 1396 (148): 64–70. (In Persian). Taalab K, Cheng T, Zhang Y. 2018. Mapping landslide susceptibility and types using Random Forest. Big Earth Data. 2(2): 159–178. Thenkabail PS, Gamage MSDN, Samakhtin VU. 2002. Evaluation of narrowband and Broadband Vegetation Indices for determining optimal hyperspectral wavebands for agricultural crop characterization, Photogrammetric engineering and remote sensing. 68 (6): 607–621. Thiery Y, Terrier M, Colas B, Fressard M, Maquaire, O., Grandjean G, Gourdier S. 2020. Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations. International Journal of Disaster Risk Reduction. 47: 1–25. Tien Bui D, Shahabi H, Omidvar E, Shirzadi A, Geertsema M, Clague JJ, ... , Lee S. 2019. Shallow landslide prediction using a novel hybrid functional machine learning algorithm. Remote Sensing. 11(8): 1–22. Vakhshoori V, Pourghasemi HR, Zare M, Blaschke T. 2019. Landslide susceptibility mapping using GIS-based data mining algorithms. Water. 11(11): 1–30. Vakhshoori V, Zare M. 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk. 7(5): 1731–1752. Vakhshoori V, Zare M. 2018. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomatics, Natural Hazards and Risk. 9(1): 249–266. Vapnik VN. 1999. The nature of statistical learning theory. Second Edition, Springer Verlag, New York, 314 p. Wang Y, Fang Z, Hong H, Peng L. 2020. Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology. 582: 1–48 Yoo Y, Baek T, Kim J, Park S. 2016. A comparative study of the frequency ratio and evidential belief function models for Landslide susceptibility mapping. Journal of the Korean Society of Surveying, Geodesy. Photogrammetry and Cartography. 34(6): 597–607. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 13(5): 839–856. | ||
|
آمار تعداد مشاهده مقاله: 447 تعداد دریافت فایل اصل مقاله: 341 |
||