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مدلسازی حساسیت به زمین‌لغزش با استفاده از شبکه‌های عصبی مصنوعی و رگرسیون لجستیک در حوضه سقزچای جنوب استان اردبیل | ||
| مهندسی و مدیریت آبخیز | ||
| مقاله 10، دوره 15، شماره 3، مهر 1402، صفحه 481-503 اصل مقاله (2.18 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwmse.2022.360475.1996 | ||
| نویسندگان | ||
| رضا طلائی* 1؛ صمد شادفر2 | ||
| 1استادیار، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اردبیل (مغان)، سازمان تحقیقات، آموزش و ترویج کشاورزی، اردبیل، ایران | ||
| 2دانشیار، پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| چکیده | ||
| مقدمه زمین‌لغزش‌ها، یکی از مخاطرات طبیعی در مناطق کوهستانی هستند که ایمنی ساکنان و محیط زیست را تهدید می‌کنند. در چند دهه گذشته، زمین‌لغزش‌ها در حوضه سقزچی در جنوب استان اردبیل، باعث وارد شدن خسارت‌هایی به منابع طبیعی و انسانی شده‌اند. زمین‌لغزش‌ها، در بیش از 9.2 درصد (2600 هکتار) از مساحت این حوضه وجود دارند. در این حوضه نیز مانند سایر مناطق زمین‌لغزشی، برای برنامه‌ریزی و مدیریت اراضی نیاز به تحلیل کل منطقه است، تا بر اساس آن احتمال وقوع زمین‌لغزش در آینده برآورد شود. حل این موضوع با تحلیل توام ویژگی‌های ژئومورفولوژی، توپوگرافی، زمین‌شناسی، کاربری اراضی، هیدرولوژی و آب و هوایی حوضه در قالب لایه‌های اطلاعاتی در محیط سامانه‌های اطلاعات جغرافیایی در یک مقیاس منطقه‌ای، امکان‌پذیر است. ارزیابی حساسیت به زمین‌لغزش در حوضه سقزچای تا به حال با روش‌های نوین و با دقت بالا انجام نگرفته است. دقت و اعتبار هر مدل، با استفاده از روش منحنی ROC و بر مبنای سطح زیر آن (AUC) تعیین شد. نتایج به دست آمده از این پژوهش، می‌تواند در پیشبینی وقوع احتمالی زمین‌لغزش و کاهش خسارت در حوضه سقزچای مورد استفاده قرار گیرد. مواد و روش‌ها حوضه مورد پژوهش، با مساحت 27918 هکتار در جنوب استان اردبیل و در جنوب غرب شهرستان خلخال واقع شده است. در این حوضه، نقشه پراکنش 113 زمین‌لغزش تهیه شد که در آن به‌ترتیب 70 و30 درصد از زمین‌لغزش‌ها به داده‌های آموزشی و ارزیابی اختصاص داده شدند. ده عامل موثر، در وقوع زمین‌لغزش‌ها شامل درصد شیب، جهات شیب، فاصله از گسل‌ها، فاصله از رودخانه‌ها، فاصله از راه‌ها، فاصله از مناطق مسکونی، واحدهای سنگ‌شناسی، بیشینه شتاب افقی زمین، کاربری اراضی و مجموع بارندگی سالانه، در تحلیل مدل‌ها مورد استفاده قرار گرفتند. به‌منظور پیشبینی حساسیت به وقوع زمین‌لغزش، از دو روش غیرخطی شبکه عصبی به نام پرسپترون چند لایه با ساختار رو به جلو و رگرسیون لجستیک، استفاده شد. بر اساس هر دو مدل، احتمال وقوع زمین‌لغزش در هر پیکسل محاسبه شد. دقت پیشبینی دو مدل با استفاده از منحنی ROC، مورد ارزیابی قرار گرفت. نتایج و بحث در مدل شبکه‌های عصبی، عوامل تشدید کننده شامل میانگین بارندگی سالانه (0.136) و بیشینه شتاب افقی زمین (0.134)، بیشترین تاثیر را در پیشبینی احتمال وقوع زمین‌لغزش‌ها داشته‌اند. عوامل فاصله از گسل‌ها (0.110)، واحدهای سنگ‌شناسی (0.109)، فاصله از راه‌ها (0.109)، فاصله از رودخانه‌ها (0.101)، فاصله از مناطق مسکونی (0.096)، جهات جغرافیایی دامنه‌ها (0.069)، کاربری اراضی (0.068) و درصد شیب دامنه‌ها (0.067) به‌ترتیب در مدلسازی حساسیت به زمین‌لغزش به روش شبکه‌های عصبی مصنوعی اهمیت دارند. بنابراین، تمامی ده عامل در مدلسازی به روش شبکه‌های عصبی مصنوعی، به‌کار گرفته شدند. نتایج به دست آمده نشان داد که احتمال وقوع زمین‌لغزش در فاصله 0.00 تا 0.961، تغییر می‌نماید. در طبقهبندی حوضه به درجات حساسیت به زمین‌لغزش، به روش شکست طبیعی بر مبنای احتمال برآوردی روش شبکه‌های عصبی، 85.7 درصد از منطقه در پهنه‌های با حساسیت کم و بسیار کم، قرار می‌گیرد. در 6.6 درصد از منطقه، احتمال حساسیت به زمین‌لغزش متوسط و در 7.7 درصد از حوضه حساسیت بالا و بسیار بالا برای وقوع زمین‌لغزش وجود دارد. تحلیل حساسیت به زمین‌لغزش به روش رگرسیون لجستیک، با روش بدون متغیر مستقل شروع شد و با اضافه کردن متغیرها در قدم دهم، خاتمه یافت. نتایج نشان می‌دهد که تنها سه سطح از عامل جهات جغرافیایی، در مدل رگرسیون لجستیک بی‌‌‌اثر هستند. با تخمین ثابت و ضرایب مربوط به متغیرهای مستقل در تحلیل رگرسیون لجستیک، مقادیر احتمال بین صفر تا یک، برای تمام پیکسل‌های منطقه محاسبه شد. با درجه‌بندی حساسیت به زمین‌لغزش به روش شکست طبیعی در مدل رگرسیون لجستیک، به‌ترتیب 79.9، 10.1 و 10 درصد از مساحت منطقه در گروه با درجات حساسیت پایین و بسیار پایین، متوسط و بالا و بسیار بالا قرار می‌گیرد. دقت و اعتبار مدل‌های رگرسیون لجستیک و شبکه عصبی مصنوعی، بر اساس منحنی ROC و سطح زیر آن (AUC) به‌ترتیب برابر 0.848 و 0.929 است. نتایج هر دو مدل، خوب بوده است و دقت بالاتر از 84 درصد داشته‌اند. نتایج به دست آمده از دو روش فوق، در اکثر مطالعات در دنیا و ایران حکایت از توانمندی آن‌ها در برآورد دقیق حساسیت احتمالی به زمین‌لغزش‌ها دارد، اما روش شبکه‌های عصبی مصنوعی، با وجود پیچیدگی‌های خاص دارای دقت بیشتری است. نتیجه‌گیری زمین‌لغزش، یک محدودیت مهم برای توسعه در مناطق لغزش‌خیز جنوب استان اردبیل است. شرایط محیطی، در حوضه سقزچی برای وقوع زمین‌لغزش‌های جدید و یا فعالیت زمین‌لغزش‌های قدیمی مستعد است. احتمال وقوع زمین‌لغزش در منطقه با استفاده از عوامل موثر و به روش رگرسیون لجستیک و شبکه عصبی مصنوعی، شبیه‌سازی شد. نتایج حاصل از مدل شبکه عصبی مصنوعی، دقیق‌تر بوده و بهتر از مدل رگرسیون لجستیک است. در مدل شبکه‌های عصبی مصنوعی، عوامل تشدید کننده زمین‌لغزش‌ها شامل میانگین بارندگی سالانه و بیشینه شتاب افقی زمین، بیشترین تاثیر را بر چگونگی پیشبینی احتمال وقوع زمین‌لغزش‌ها دارند. روش شبکه عصبی مصنوعی، در تبیین رابطه وقوع زمین‌لغزش با عوامل موثر، برتری نشان داد. نقشه حساسیت خروجی از این مدل، به پنج طبقه حساسیت بسیار کم (71.4 درصد)، کم (14.3 درصد)، متوسط (6.6 درصد)، زیاد (4.3 درصد) و بسیار زیاد (3.4)، تقسیم شد. استفاده از مدل‌های شبکه عصبی مصنوعی، در ارزیابی حساسیت به زمین‌لغزش در حوضه و مناطق مشابه، به‌منظور کمک به تصمیم‌گیران، برنامه‌ریزان، مدیران کاربری اراضی و سازمان‌های دولتی در کاهش خطرات و آسیب‌ها، توصیه می‌شود. | ||
| کلیدواژه‌ها | ||
| احتمال؛ ارزیابی؛ پیشبینی؛ خسارت؛ دقت؛ عوامل موثر و مخاطرات طبیعی | ||
| عنوان مقاله [English] | ||
| Landslide susceptibility modeling using artificial neural network and logistic regression methods at the Saqezchay Basin, south of Ardabil Province | ||
| نویسندگان [English] | ||
| Reza Talaei1؛ Samad Shadfar2 | ||
| 1Assistant Professor, Soil Conservation and Watershed Management Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran | ||
| 2Associate Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Landslides are one of the natural hazards in mountainous areas that threaten the safety of residents and the environment. In the past few decades, landslides have caused damage to natural and human resources in the Saqezchi Basin in the south of Ardabil Province. Landslides have occurred in more than 9.2% (2600 ha) of the area. In this basin, like other landslide areas, for land-use planning and management, it is necessary to analyze the whole area in order to estimate the probability of landslides occurrence in the future. It is possible to solve this problem by analyzing the geomorphology, topography, geology, land use, hydrology and climate factors of the basin in the form of information layers in the geographic information systems on a regional scale. Landslide susceptibility assessment has not been done with modern methods and with high accuracy in the Saqzachai Basin until now. The results of this research can be used in predicting the possible occurrence of landslides and reducing damage in the Saqzachai Basin. Materials and methods The research basin with an area of 27,918 ha is located in the south of Ardabil Province and in the southwest of Khalkhal City. In this basin, the inventory map was generated based on 113 landslides, the training dataset and validation dataset were, respectively, prepared using 70% landslides and the remaining 30% landslides. Ten landslide causative factors based on slope angle, slope aspect, distance to faults, distance to stream network, distance to the roads, distance to settlement area, lithology, land-use, peak ground acceleration (PGA) and average annual precipitation were applied for the models analysis. Two nonlinear methods of neural network called multi-layer perceptron with feed forward structure and logistic regression were used to predicting the susceptibility of landslide occurrence. The probability of landslide occurrence in each pixel was calculated based on both models. The prediction accuracy of the two models were evaluated using the Receiver Operating Characteristic (ROC) curve. Results and discussion In the neural network model, landslides triggering factors, including the average annual precipitation (0.136) and the peak ground acceleration (0.134), have been the greatest effect in predicting the probability of landslides. The factors of slope angle (0.067), slope aspect (0.069), distance to faults (0.110), distance to stream network (0.101), distance to the roads (0.109), distance to settlement area (0.096), lithology (0.109) and land-use (0.068) are respectively important in landslides susceptibility modeling to using artificial neural networks. Therefore, all ten factors were used in modeling by artificial neural networks. The results indicated that the probability of landslide occurrence varies from 0.00 to 0.961. In the classification of the watershed according to the degree of landslide susceptibility by the natural breaks method based on the estimated probability by the neural network method, 85.7% of the area is placed in the zones with low and very low susceptibility. In 6.6% of the area, there is a probability of moderate susceptibility, and in 7.7%, there is a high and very high landslide susceptibility. Landslide susceptibility analysis is started without independent variable and ended by adding variables in the tenth step using logistic regression method. The results show that only three levels of the factor of slope aspect are ineffective in the logistic regression model. Probability values were calculated between 0 and 1 for all pixels in the area based on the values of independent variables by estimating constant and coefficients related to logistic regression model. The landslide-prone areas of low and very low susceptibility, medium susceptibility and high to extremely high-susceptibility grades are 79.9%, 10.1%, and 10%, of area, respectively, by the natural breaks method in the logistic regression model. The accuracy and validity of the logistic regression and artificial neural network models based on the ROC curve and the area under it (AUC) are equal to 0.848 and 0.929, respectively. The findings of the models show good results with the accuracy of two models being higher than 84%. The results obtained from two methods in most studies in the world and in Iran indicate their ability to accurately estimate susceptibility to landslides occurrence, but the artificial neural network method is more accurate despite its specific complexities. Conclusion Landslides are an important limitation for development in the landslide areas in the south of Ardabil Province. The environmental conditions in the Saqzachi Basin are susceptible to the occurrence of new landslides or the reactivation of old landslides. The probability of landslides occurrence was simulated using effective factors and using logistic regression and artificial neural network models in the region. The results obtained from the artificial neural network model are the most accurate and better than the logistic regression model. The landslides triggering factors, including the average annual precipitation and the peak ground acceleration have the greatest impact to predicting the probability of landslide occurrence using the artificial neural network model. The findings of the models show good results with the accuracy of two models being higher than 84%. The artificial neural network method is superior in explaining the relationship between landslide occurrence and influencing factors. The landslide susceptibility map was prepared using this method by dividing into five class, namely: very low (71.4%), low (14.3%), moderate (6.6%), high (4.3%) and very high (3.4%) susceptibility zones. Therefore, it is recommended to use the artificial neural network models in landslide susceptibility assessment in the basin and similar regions to help decision makers, planners, land use managers and government agencies in hazard and damage reduction. | ||
| کلیدواژه‌ها [English] | ||
| Accuracy, Assessment, Damage, Effective factors, Natural hazards, Prediction and probability | ||
| مراجع | ||
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Aditian, A., Kubota, T., Shinohara, Y., 2018. Comparison of GIS-based landslide susceptibilitymodels using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318, 101-111. Ajim Ali, S., Parvin, F., Vojteková, J., Costache, R., Thi, N., Linh, T., Pham, Q.B., Vojtek, M., Gigović, L., Ahmad, A., Ghorbani, M.A., 2021. GIS-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci. Front. 12(2), 857-876. Ansari, F., Blurchi, M.C., 1996. Landslides of Ardabile Province, Iran. Geological Survey of Iran, Iran (in Persian). Ayalew L., Yamagishi, H., Marui, H., Kanno, T., 2005. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. J. Eng. Geol. 81, 432-445. Ayalew, L., Yamagishi, H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65, 15-31. Barella, C.F., Sobreira, F.G., Zêzere, J.L., 2019. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil. Bull. Eng. Geol. Environ. 78, 3205-3221. Bravo-López, E., Fernández Del Castillo, T., Sellers, C., Delgado-García, J., 2022. Landslide susceptibility mapping of landslides with artificial neural networks: multi-approach analysis of backpropagation algorithm applying the neuralnet package in Cuenca, Ecuador. J. Remote Sens. 14(3495), 1-30. Chen, W.W., Zhang, S., 2021. GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. Catena 203, 105344. Chen, X., Chen, W., 2021. GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena 196, 104833. Davis, J.C., Ohlmacher, G.C., 2002. Landslide hazard prediction using generalized logistic regression. Proceedings of 8th Annual Conference of the International Association for Mathematical Geology, Berlin, Germany. Demir, G., 2018. Landslide susceptibility mapping by using statistical analysis in the north Anatolian fault zone (NAFZ) on the northern part of Suşehri Town, Turkey. Nat. Hazards 92, 133-154. Dou, J., Yunus, A.P., Bui, D.T., Merghadi, A., Sahana, M., Zhu, Z., Chen, C.W., Han, Z., Pham, B.T., 2020. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17, 641-658. Falaschi, F., Giacomelli, F., Federici, P.R., Puccinelli, A., D’Amato Avanzi, G., Pochini, A., Ribolini, A., 2009. Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat. Hazards 50, 551-569. Galeandro, A., Doglioni, A., Simeone, V., Šimůnek, J., 2014. Analysis of infiltration processes into fractured and swelling soils as triggering factors of landslides. Environ. Earth Sci. 71, 2911-2923. Gómez, H., Kavzoglu, T., 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. J. Eng. Geol. 78, 11-27. Grozavu, A., Mărgărint, M.C., Patriche, C.V., 2012. Landslide susceptibility assessment in the brăieşti-sineşti sector of iaşi cuesta. Carpathian J. Earth Environ. Sci. 5(2), 61-70. Hagan, T.M., Demuth, B.H., Beale, H.M., De Jesús, O., 2014. Neural network design, 2nd (ed). Electrical Engineering Series. Hashemi Tabatabaei, S., 1998. Landslide hazard zonation in southwest of Ardabil Province in Iran. Ministry of Roads and Urban Development, Tehran, Iran (in Persian). Hemmati, R., Dolatimehr, A., Nasirifar, A., Shahbazi, M., Hezhabrpour, Gh., Aghaei, Kh., 2007. Ardabil Province climate. Applied Meteorology Research Center of Ardabil, Islamic Respublication of Iran Meteorological Organization, Ministry of Roads and Urban Development, Iran (in Persian). Hong, H.Y., Liu, J.Z., Zhu, A.X., 2019. Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China). Environ. Earth Sci. 78(15),1-20. Huang, F.M., Cao, Z.S., Guo, J.F., Jiang, S.H., Li, S., Guo, Z.Z., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580. Jianqiang, Z., Yonggang, G., Yong, L., Qiang, Z., Yuhong, J., Huayong, C., Xiaoqing, C., 2022. Zonation-based landslide hazard assessment using artificial neural networks in the China-Pakistan Economic Corridor. Front. Earth Sci. 10(927102), 1-15. John, R.D., Anne-Gaelle, A., James, D.S., Lavs, B., 2006. Validation of a region-wide model of landslide susceptibility in the Manawatu-Wanganui region of New Zealand. Geomorphology 1-4, 70-79. Khosravi, M., Jamali, A.A., 2019. forecasting the trend of landslide changes in the northern region of quchan with regard to the factors affecting landslide using neural network, cellular automata-markov, and regression logistics. J. Geol. and Environ. Hazards 7(3), 1-17 (in Persian). Lee, S., 2007. Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf. Process. Landf. 32, 2133-2148. Lee, S., Min, K., 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geol. 40 (9): 1095-1113. Lee, S., Pradhan, B., 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4, 33-41. Li, B., Wang, N., Chen, J., 2021. GIS-based landslide susceptibility mapping using information, frequency ratio, and artificial neural network methods in Qinghai Province, northwestern China. Adv. Civ. Eng. Article ID 4758062: 1-14. Ling, S., Zhao, S., Huang, J., Zhang, X., 2022. Landslide susceptibility assessment using statistical and machine learning techniques: a case study in the upper reaches of the Minjiang River, southwestern China. Front. Earth Sci. 10, 986172. Liu, Y.L., 2010. Application of logistic regression and artificial neural networks in spatial assessment of landslide hazards. Hydrogeol. Engin. Geol. 37(5), 92-96. Mathew, J., Jha, V.K., Rawat, G.S., 2009. Landslide susceptibility zonation mapping and its validation in part of garhwal lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6, 17-26. Meinhardt, M., Fink, M., Tünschel, H., 2015. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234, 80-97. Menard, S., 2002. Applied logistic regression analysis, 2nd (ed.). Sage University Paper Series on Quantitative Applications in Social Sciences, vol. 106, Thousand Oaks, California, USA. Meten, M., Bhandary, N.P., Yatabe, R., 2015. GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in Central Ethiopia. J. Mt. Sci. 12(6), 1355-1372. Nhu, V.H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W.S., Miraki, J., Dou, C., Luu, K,. Górski, B., Thai Pham, H., Nguyen, D., Ahmad, B.B., 2020. Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Int. J. Environ. Res. Public Health 17(8), 1-30. Nikandish, N., Mir Sanei, R., 1996. Introduction to Ardabile Province landslides. Iran Ministry of Jihad-e- Agriculture, Tehran, Iran (in Persian). Norusis, M.J., 2006. SPSS 15.0 guide to data analysis. Pearson Education (US) Publisher, USA. Pham, B.T., Bui, D.T., Prakash, I., 2017. Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech. Geol. Eng. 35(6), 2597-2611. Polykretis, C., Ferentinou, M., Chalkias, C., 2015. A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull. Eng. Geol. Environ. 74, 27-45. Pourghasemi, H.R., Rahmati, O., 2018. Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162, 177-192. Rai, D.K., Xiong, D., Zhao, W., Zhao, D., Zhang, B., Mani Dahal, N., Wu, Y., Aslam Baig, M., 2022. An ınvestigation of landslide susceptibility using logistic regression and statistical ındex methods in Dailekh District, Nepal. Chinese Geographical Science 32, 834-851. Rana, H., Babu, G.L.S., 2022. Regional back analysis of landslide events using TRIGRS model and rainfall threshold: an approach to estimate landslide hazard for Kodagu, India. Bull. Eng. Geol. Environ. 81(4), 160. Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T., Akgun, A., 2014. Application of frequency ratio, statistical index, and weights-ofevidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab. J. Geosci. 7(2), 725-742. Saha, S., Arabameri, A, Saha, A., Blaschke, T., Ngo, P.T.T., Nhu, V.H., Band, S.S., 2021. Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on crossvalidation method. Sci. Total Environ. 764, 142928. Saro, L., Seong, J., Woo, O., Young, K., Moung-Jin, L., 2016. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: a casestudy of Inje, Korea. Open Geosci. 8, 117-132. Sdao, F., Lioi, D.S., Pascale, S., Caniani, D., Mancini, I.M., 2013. Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat. Hazards Earth Syst. Sci. 13, 395-407. Sepah Vand, A.R., Moradi, H.R., Abdolmaleki, P., 2017. Landslide hazard mapping using the artificial neural network a part of Haraz Watershed. Watershed Manag. Res. (Pajouhesh and Sazandegi) 29(4)-113, 9-19 (in Persian). Shirani, K., Arabameri, A.R., 2015. Landslide hazard zonation using logistic regression method, case study: Dez-e-Oulia Basin. J. Water Soil Sci. 19 (72), 321-335 (in Persian). Shirani, K., Heydari, F., Arabameri, A., 2017. Comparison of artificial neural network and multivariate regression methods in landslide hazard zonation, case study: Vanak Basin, Isfahan Province. J. Watershed Engin. Manage. 9(4), 45-464 (in Persian). Shirani, K., Naderi Samani, R., 2022. Determination of effective factors and assessment of landslide susceptibility using random forest and artificial neural network in Doab Samsami region, Chaharmahal va Bakhtiari Province. Watershed Manage. Res. J. 35(1), 40-60 (in Persian). Su, C., Wang, L., Wang, X., Huang, Z., Zhang, X., 2015. Mapping of rainfallinduced landslide susceptibility in Wencheng, China, using support vector machine. Nat. Hazards 76, 1759-1779. Talaei, R., 2018. A combined model for landslide susceptibility, hazard and risk assessment. AUT J. Civil Engin. 2(1), 11-28. Talaei, R., Ghayoumian, J., Shariat Jafari, M., Aliakbarzadeh, E., 2004. Study on effective factor causing landslide in southwest of Khalkhal region. Agriculture Research and Education Organization, Ministry of Jahad-e-Agriculture, Tehran, Iran (in Persian). Tanyu, B.F., Abbaspour, A., Alimohammadlou, Y., Tecuci, G., 2021. Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets. Catena 203, 105355. Thiery, Y., Maquaire, O., Fressard, M., 2014. Application of expert rules in indirect approaches for landslide susceptibility assessment. Landslides 11, 411-424. Tian, Y.Y., Xu, C., Chen, J., Zhou, Q., Shen, L.L., 2017. Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian, Gansu, China, Mw 5.9 event. Landslides 14, 1915-1927. Türköz, M., Tosun, H., 2011. A GIS model for preliminary hazard assessment of swelling clays, a case study in Harran Plain (SE Turkey). Environ. Earth Sci. 63(6), 1343-1353. Uromeihy, A., Mahdavifar, M.R., 2000. Landslide hazard zonation of the Khorshrostam area, Iran. Bull. Eng. Geol. Environ. 58, 207-213. Wahono, B.F.D., 2010. Applications of statistical and heuristical methods for landslide susceptibility assessments: a case study in Wadas Lintang sub district, Wonosobo Regency, Central Java Province, Inonesia. MSc Thesis, Gadjah Mada University, International Institute for Geo-Information and Earth Observation. Wang, L.J., Guo, M., Sawada, K., Lin, J., Zhang, J., 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci. J. 20, 117-136. Xie, P., Wen, H., Ma, C., Baise, L.G., Zhang, J., 2018. Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China. Geomatics Nat. Hazards Risk 9(1), 501-523. Xu, C., Xu, X., Dai, F., Wu, Z., He, H., Shi, F., Wu, X., Xu, S., 2013. Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Nat. Hazards 68, 883-900. Yesilnacar, E., Topal, T., 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engin. Geol. 79(3-4), 251-261. Yilmaz, I., 2009. A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull. Eng. Geol. Environ. 68, 297-306. Zhang, Y.S., Dong, S.W., Hou, C.T., Guo, C.B., Yao, X., Li, B., Du, J.J., Zhang, J.G., 2013. Geohazards induced by the Lushan Ms7.0 earthquake in Sichuan Province, Southwest China: typical examples, types and distributional characteristics. Acta Geol. Sin. 87(3), 646-657. Zhang, J., van Westen, C.J., Tanyas, H., Mavrouli, O., Ge, Y., Bajrachary, S., Gurunget, D.R., Dhital, M.R., Khanal, N.R., 2019a. How size and trigger matter: analyzing rainfall- and earthquake-triggered landslide inventories and their causal relation in the koshi river basin, central himalaya. Nat. Hazards Earth Syst. Sci. 19(8), 1789-1805. Zhang, T., Han, L., Zhang, H., Zhao, Y., Li, X., Zhao, L., 2019b. GIS based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. J. Mt. Sci. 16, 1275-1288. Zhang, T., Li, Y., Wang, T., Wang, H., Chen, T., Sun, Z., Luo, D., Li, C., Han, L., 2022. Evaluation of different machine learning models and novel deep learning‑based algorithm for landslide susceptibility mapping. Geosci. Lett. 9(26) 1-16. | ||
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