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مدل سازی حساسیت زمین لغزش با الگوریتم یادگیری ماشین جنگل تصادفی در آبخیز سد رئیسعلی دلواری | ||
| پژوهش های آبخیزداری | ||
| مقاله 2، دوره 33، شماره 1 - شماره پیاپی 126، فروردین 1399، صفحه 2-13 اصل مقاله (644.83 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.126288.1219 | ||
| نویسندگان | ||
| ناصر حیدری1؛ محمود حبیب نژاد2؛ عطا ا.. کاویان2؛ حمید رضا پورقاسمی* 3 | ||
| 1دکترای علوم و مهندسی آبخیزداری، دانشکدهی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران | ||
| 2استاد گروه علوم و مهندسی آبخیزداری، دانشکدهی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران | ||
| 3دانشیار بخش مهندسی منابع طبیعی و محیط زیست، دانشکدهی کشاورزی، دانشگاه شیراز، شیراز، ایران | ||
| چکیده | ||
| هدف از این پژوهش مدلسازیکردن مکانی حساسیت زمینلغزش با الگوریتم یادگیری ماشین جنگل تصادفی و اولویتبندی کردن عاملهای موثر بر وقوع آن در آبخیز سد رئیسعلی دلواری است. نقشهی پراکنش زمینلغزشهای منطقه با بازدیدهای صحرایی و بانک اطلاعات زمینلغزشهای کشور تهیه شد. در مجموع از 279 زمینلغزش شناختهشده 70% (195) آن برای مدلسازی و 30% (84) مانده برای ارزیابی مدل بهکاربرده شد. لایههای اطلاعاتی ارتفاع، جهت شیب، درجهی شیب، انحنای سطح، انحنای نیمرخ، شاخص رطوبت پستیوبلندی، فاصله از شبکهی آبراه، تراکم زهکشی، فاصله از گسل، فاصله از جاده، زمینشناسی و شاخص تفاضلی پوشش گیاهی بهنجارشده انتخاب شد. مدل جنگل تصادفی بر اساس ارتباط بین متغیر وابسته (زمینلغزشها) و متغیرهای مستقل (عاملهای موثر) در نرمافزار R و با بستهی نرمافزاری Random Forest اجرا، و نقشهی حساسیت زمینلغزش تهیه شد. مدل با بهکاربردن منحنی تشخیص عملکرد نسبی و 30% از دادههای لغزشی بهکاربردهنشده در فرآیند مدلسازی ارزیابی شد. نتایج ارزیابی نشاندهندهی دقت عالی مدل جنگل تصادفی 0/983 (3/98%) بود. اولویتبندی عاملهای موثر اهمیت درجهی شیب، ارتفاع، انحنای نیمرخ، فاصله از جاده و واحدهای سنگشناسی را نشانداد. بنابراین بهنظر میرسد که نقشهی حساسیت زمینلغزش تهیهشده ممکن است نقش بسزایی در تصمیمگیریهای مدیران برای آمایشکردن سرزمین و مدیریتکردن جامع آبخیز سد رئیسعلی دلواری داشته باشد. | ||
| کلیدواژهها | ||
| آبخیز سد رئیسعلی دلواری؛ جنگل تصادفی؛ حساسیت زمین لغزش؛ میانگین کاهشی دقت | ||
| عنوان مقاله [English] | ||
| Landslide Susceptibility Modelling Using the Random Forest Machine Learning Algorithm in the Watershed of Rais-Ali Delvari Reservoir | ||
| نویسندگان [English] | ||
| Naser Heydari1؛ Mahmoud Habibnejad2؛ Ataollah Kavian2؛ Hamid Reza Pourghasemi3 | ||
| 1PhD Graduated, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran | ||
| 2Professor, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran | ||
| 3Associate Professor, Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran | ||
| چکیده [English] | ||
| The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in the watershed of the Rais-Ali Delvari Reservoir. The landslide inventory map was prepared using extensive field surveys and the Iranian Landslides Working Party Data Bank. Of the total of 279 identified landslide locations, 70% were used for the modelling processes and the remaining (30%) were applied for validation of the developed model. Different thematic layers including elevation, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, drainage density, distance from faults, distance from roads, lithological units, and the normalized difference vegetation index (NDVI) were selected. According to the relationship between the dependent (landslides) and the independent (effective factors) variables in the R statistical software, the random forest algorithm was run using the “Random Forest” package, and a landslide susceptibility map was prepared. Accuracy of the model was tested using the receiver operating characteristic (ROC) curve based on 30% of unused landslides in the modelling process. Accuracy results indicated that the Random Forest model with an AUC value of 0.983 had an excellent precision. Also, prioritization of the effective factors showed that the slope angle, elevation, plan curvature, distance from road, and lithological units had the highest effect on landslide occurrence. Therefore, it maybe suggested that the prepared landslide susceptibility map could be effective in decision making for land use planning, and in the managing of the Rais-Ali Delvari Reservoir Watershed. | ||
| کلیدواژهها [English] | ||
| Landslide susceptibility, mean decrease accuracy, Random Forest, Rais-Ali Delvari Reservoir Watershed | ||
| مراجع | ||
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Ahmadi H, Mohammadkhan S. 2003. Determining the factors affecting mass movements (Case study: Taleghan Watershed). Iranian Journal of Natural Resources. 55(4): 455–466. Amiri M, Pourghasemi HR, Ghanbariana GA, Afzali SF. 2019. Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma. 340: 55–69. Arabameri AR, Pradhan B, Pourghasemi HR, Rezaei Kh, Kerle N. 2018. Spatial modelling of gully erosion using GIS and R programing: A comparison among three data mining algorithms. Applied Sciences. 8(8): 13–69. https://doi.org/10.3390/app8081369. Breiman L. 2001. Random forests. Machine Learning. 45: 5–32. Chau KT, Chan JE. 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides. 2: 280–290. Chen W, Xie X, Wang J, Pradhan B, Hong H, Tien Bui D, Duan Z, Ma J. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena. 151: 147–160. Davoudi Moghaddam D, Pourghasemi HR, Rahmati O. 2019. Assessment of the contribution of geo-environmental factors to flood inundation in a semi-arid eegion of SW Iran: Comparison of different advanced modeling approaches. Natural hazards GIS-based spatial modeling using data mining techniques. In H.R. Pourghasemi, and M. Rossi (Eds.), Natural hazards GIS-based spatial modeling using data mining techniques. Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H. 2006. Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology. 51: 241–256. Dymond JR, Ausseeil AG, Shepherd JD, Buettner l. 2006. Validation of a region- wide model of landslide susceptibility in the Manawatu- Wanganui Region of New Zealand. Geomorphology. 74: 70–79 Erner A, Sebnem H, Duzgun B. 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides. 7: 55–68. Fatemi Aghda SM, Ghayoumian J, Ashghali Farahani A. 2003. Evaluation of statistical methods in landslide hazard analysis. Geosciences. 82 (11): 28–47. Feiznia S, Kalarstaghi A, Ahmadi H, Safaei M. 2004. An investigation of effective factors on landslide occurrence and landslide hazard zonation (case study: Shirin Rood Drainage Basin - Tajan Dam). Iranian Journal of Natural Resources. 57 (1): 3–22. Garosi Y, Sheklabadi M, Besalatpour AA, Pourghasemi HR, Conoscenti C, Van Oost K. 2018. Comparison of the different resolution and source of controlling factors for gully erosion susceptibility mapping. Geoderma. 330: 65–78. Hong H, Pourghasemi HR, Pourtaghi ZS. 2016. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical. Geomorphology. 259: 105–118. Iranian Landslide Working Party (ILWP). 2007. Iranian landslides list. Forest, Rangeland and Watershed Association, Tehran, Iran, 60 p. Koehorst BAN, Kjekstad O, Patel D, Lubkowski Z, Knoeff JG. Akkerman GJ. 2005. Work package 6, Determination of Socio-Economic Impact of Natural Disasters, Assessing socio-economic Impact in Europe, 173 p. Lagomarsino D, Tofani V, Segoni S, Catani F, Casagli N. 2017. A tool for classification and regression using random forest methodology: applications to landslide susceptibility mapping and soil thickness modeling. Environmental Modeling and Assessment. 22(3): DOI: 10.1007/s10666-016-9538-y Lee S. 2007. Application and verification of fuzzy algebraic operators to landslide susceptibility mapping, Environmental Geology. 52: 615–623. Moore ID, Grayson RB, Ladson AR. 1991. Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes. 5: 3–30. Naghibi A, Pourghasemi HR. 2015. A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods for groundwater potential mapping in Iran. Water Resources Management. 29 (14): 5217–5236. National Cartographic Center. 2010. Topographic map of Raiisali Delvari Reservoir. Nefeslioglu HA, Gokceoglu C, Sonmez H. 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology. 97: 171–191. Neuhauser B, Terhorst B. 2006. Landslide susceptibility assessment using weights-of- evidence applied to a study area at the Jurassic Escarpment (SW-Germany). Geomorphology. 86 (1–2): 12–24. Nicodemus KK. 2011. Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures. Briefings in Bioinformatics. 12: 369–373. Shadfar S, Yamani M, Namaki M. 2005. Landslide hazard zonation using information value, area density, and LNRE models in Chalekroud watershed. Water and Watershed. 3: 62–68 Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest Ecology and Management. 275:117–129. Park S, Kim J. 2019. Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Applied Sciences. 9: 942 doi:10.3390/app9050942 Pourghasemi HR, Kerle N. 2016. Random Forest-evidential belief function based landslide susceptibility assessment in western Mazandaran Province, Iran. Environmental Earth Sciences. 75: 185. DOI: 10.1007/s12665-015-4950-1 Pourghasemi HR, Pradhan B, Gokceoglu C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran, Natural Hazards. 63 (2): 965–996. Pourghasemi HR, Rahmati A. 2018. Rapid GIS-based spatial and regional modelling of landslide susceptibility using machine learning techniques in the R open source software. Catena. 162: 177–192. Pourghasemi HR, Rossi M. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical Applied Climatology. 130 (1–2): 609–633. Rahmati O, Pourghasemi HR, Melesse A. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. Catena. 137: 360–372. Rahmati O, Tahmasebipourb N, Haghizadeh A, Pourghasemi HR, Feizizadeh B. 2017. Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of SW Iran, using rule-based semi-automated technique and conditional probability model. Science of the Total Environment. 579: 913–927. Shataee S, Weinaker H, Babanejad M. 2011. Plot- level forest volume estimation using airborne laser scanner and TM data, comparison of boosting and random forest regression algorithms. Procedia Environmental Sciences. 7: 68–73. Taalab K, Cheng T, Zhang Y. 2018. Mapping landslide susceptibility and types using Random Forest. Big Earth Data. 2: 1–20. Vorpahl P, Elsenbeer H, Märker M, Schröder B. 2012. How can statistical models help to determine driving factors of landslides? Ecological Modelling. 239: 27–39. Xu C, Dai F, Xu X, Lee YH. 2012. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology. 145–146: 70–80. Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena. 72: 1–12. Yesilnacar EK. 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, 423 p. Yilmaz C, Topal T, Suzen ML. 2012. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences. 65: 2161–2178. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. 2015. 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. | ||
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