آمار
| تعداد نشریات | 14 |
| تعداد شمارهها | 674 |
| تعداد مقالات | 7,006 |
| تعداد مشاهده مقاله | 10,318,170 |
| تعداد دریافت فایل اصل مقاله | 9,516,962 |
Efficiency of the maximum entropy algorithm and GIS in assessing landslide susceptibility | ||
| مجله پژوهشهای حفاظت آب و خاک | ||
| دوره 28، شماره 4، دی 1400، صفحه 53-76 اصل مقاله (1.48 M) | ||
| نوع مقاله: مقاله کامل علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22069/jwsc.2022.19292.3478 | ||
| نویسندگان | ||
| مهدیس امیری1؛ حمید رضا عسگری* 1؛ حمیدرضا پورقاسمی2؛ چوقی بایرام کمکی3 | ||
| 1گروه آبخیزداری و مدیریت مناطق بیابانی- دانشکده مرتع و آبخیزداری- دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| 2دانشگاه شیراز دانشکده کشاورزی | ||
| 3هیات علمی/ دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| چکیده | ||
| Background and Objectives: A review of the damages caused by landslides proves the need to investigate the factors affecting the occurrence of this phenomenon and the need to predict its occurrence. Landslides are one of the most dangerous natural disasters that cause excessive human and financial losses in the mountains worldwide. Due to their dangerous nature, landslides suddenly disrupt the morphology and cause major damage to residential areas, roads, agricultural lands, etc., in mountainous areas. Fortunately, there are appropriate methodologies for assessing risk and determining the effective risk factors associated with them. Materials and Methods: In this study, the maximum entropy of three replications was applied to Maxent software to investigate landslide susceptibility in the southern areas of the Fars Province, Iran. Thirteen factors were used to prepare the landslide susceptibility map: lithological units (Lu), land use/land cover (LULC), slope percentage (SP), slope aspect (SA), altitude, plan curvature (Plan-C), topographic wetness index (TWI), distance to river (DTR), distance to roads (DTRS), distance to fault (DTF), drainage density (DD), normalized difference vegetation index (NDVI), and annual mean rainfall (AMR). In this study, the lack of multicollinearity among the effective factors was proven using tolerance (TOL) and variance inflation factor (VIF) indicators. In addition, the weights of these 13 factors were determined using the analytic hierarchy process (AHP) model. Results: The results of the AHP method show that, in descending order, lithological units, land use-cover, and slope percentage are the most important factors influencing the occurrence of landslides in the study area. Thirty percent of the landslide points were randomly selected, removed from the modeling data, and used for the evaluation using the ROC/AUC indicator. In addition, the final map of the landslide susceptibility was presented in three scenarios using data replication. The preparation of three different outputs had good accuracy, but the third iteration, with an AUC value of 0.778 (ROC= 77.8%), had the highest accuracy in preparing the landslide susceptibility map. The evaluation of landslide susceptibility maps using the second and third iterations, with AUC values of 0.77 (ROC= 77 %) and 0.640 (ROC= 64%), respectively, had good and moderate accuracy with the highest efficiency in predicting landslide sensitivity. Finally, the highest percentage of landslide susceptibility area according to the first, second, and third repetitions were, respectively, in the moderate sensitivity class (0.03-0.1) with the value of 26.14%, in the moderate sensitivity class (0.04-0.4) with a value of 25.91%, and in the moderate sensitivity class (0.04-0.1) with a value of 25.71%, which was the highest percentage of the landslide area. Conclusion: In general, landslides, due to their dangerous nature, suddenly disrupt the morphology of an area and cause major damage that can be measured in the lithological units of the study area, land-use change, and slope percentage. Therefore, landslides are a complex process that has a devastating effect on the environment and human life and requires more investigation and preventive measures. Keywords: Mass movements mapping, Machin learning, AHP, Effective factors, ROC-AUC indicator, Fars Province | ||
| کلیدواژهها | ||
| "Mass movements mapping؛ Machin learning؛ AHP؛ Effective factors؛ ROC-AUC indicator" | ||
| مراجع | ||
|
1.Akgun, A., Dag, S., and Bulut, F. 2008. Landslide susceptibility mapping for alandslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ. Geol. 54: 1127-1143.
2.Alilou, H., Rahmati, O., Singh, V.P., Choubin, B., Pradhan, B., Keesstra, S., Ghiasi, S.S., and Sadeghi, S.H. 2019. Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. J. Environ. Manag. 232: 22-36.
3.Arabameri, A., Pradhan, B., Rezaei, K., Lee, S., and Sohrabi, M. 2019. An ensemble model for landslide susceptibility mapping in a forested area. Geocarto Int. pp. 1-25.
4.Banavar, J.R., Maritan, A., and Volkov, I. 2010. Applications of the principle of maximum entropy: from physics to ecology. J. Phys. Condens. Matter.22 (6), 063101.
5.Beguería, S. 2006. Changes in land cover and shallow landslide activity: acase study in the Spanish Pyrenees. Geomorphology. 74: 196–206.
6.Blahut, J., Van Westen, C.J., and Sterlacchini, S. 2010. Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology. 119 (1-2): 36-51. https://doi.org/10.1016/j.geomorph.2010.02.017.
7.Blaschke, T., and Montanarella, L. (Eds.), SAGA - Seconds Out (=Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, 19), pp. 23-32.
8.Bordoni, M., Meisina, C., Valentino, R., Bittelli, M., and Chersich, S. 2015.Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS. Nat. Hazards Earth Syst. Sci. 15p.
9.Boubli, J.P., and De Lima, M.G. 2009. Modeling the geographical distribution and fundamental niches of Cacajao spp. and Chiropotes israelita in Northwestern Amazonia via a maximum entropy algorithm. International Journal of Primatology, 30 (2): 217-228.
10.Bowen, W.M. 1993. AHP: Multiple Criteria Evaluation in Klosterman. New Brunswick: Center for Urban Policy Research.
11.Brardinoni, F., Slaymaker, O., and Hassan, M.A. 2003. Landslide inventory in a rugged forested watershed: a comparison between air-photo andfield survey data, Geomorphology,54: 179-196.
12.Brenning, A. 2008. Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. In: Böhner, J.
13.Broeckx, J., et al. 2016. Linking landslide susceptibility to sediment yield at regional scale: application to Romania. Geomorphology. 268: 222-232.
14.Bui, D.T., et al. 2015. A novel hybrid evidential belief function-based fuzzy logic modelin spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomat. Nat. Haz. Risk. 6: 243–271.
15.Cao, B., Bai, C., Zhang, L., Li, G., and Mao, M. 2016. Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China. J. Plant Ecol. rtw009.
16.Chang-Jo, F.C., and Fabbri, A.G. 2003. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30 (3): 451-472.
17.Chen, W., Li, X., Wang, Y., Chen, G., and Liu, S. 2014. Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China. Remote Sens. Environ. 152: 291-301.
18.Chen, W., Pourghasemi, H.R., and Naghibi, S.A. 2017.Prioritizationof landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bull. Eng. Geol. Environ. pp. 1-19. 19.Chen, W., Shahabi, H., Shirzadi, A., Li, T., Guo, C., Hong, H., Li, W., Pan, D., Hui, J., Ma, M., Xi, M., and Ahmad, B.B. 2018. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int. 33 (12): 1398-1420.
20.Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Li, Sh., Jaafari, A., and Ahmad, B.B. 2019. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena 172: 212-231. https://doi.org/10. 1016/j.catena. 2018.08.025.
21.Chung C.J.F., and Fabbri, A.G. 2003. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 30(3): 451-472. doi:10.1023/ B: NHAZ.0000007172.62651.2b.
22.Costanza, J., Beck, S., Pyne, M., Terando, A., Rubino, M., White, R.,and Collazo, J. 2016. Assessing Climate-Sensitive Ecosystems inthe Southeastern United States (No. 2016-1073). US Geological Survey.
23.Dou, J., et al. 2015. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat. Hazards,78: 1749-1776.
24.Duc, D.M. 2012. Rainfall-triggered large landslides on 15 December 2005 inVan Canh District, Binh Dinh Province, Vietnam. Landslides, 10: 219-230.
25.Elith, J.H., Graham, C.P., Anderson, R., Dudík, M., Ferrier, S., Guisan, A.J., Hijmans, R., Huettmann, F.R., Leathwick, J., Lehmann, A., et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129-151.
26.Elkadiri, R., Sultan, M., Youssef, A., Elbayoumi, T., Chase, R., Bulkhi, A., and Al-Katheeri, M. 2014. A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. Selected topics in applied earth observations and remote sensing, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi:10.1109/JSTARS. 2014. 2337273. 27.Erener, A., and Düzgün, H.S.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 (1): 55–68.
28.Felicísimo, A., Cuartero, A., Remondo, J., and Quirós, E. 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods:a comparative study. Landslides,10: 175-189.
29.Fukuoka, M. 1980. Landslides associated with rainfall. Geotech.Eng. 11 (1).
30.Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., and Reichenbach, P. 2008. Comparing landslide inventory maps. Geomorphology, 94 (3): 268-289.
31.Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., and Reichenbach, P. 2008. Comparing landslide inventory maps. Geomorphology, 94: 268-289.
32.Guzzetti, F., Cesare, A., Cardinali, M., Fiorucci, F., Santangelo.
33.He, Q., Shahabi, H., Shirzadi, A., Li, S., Chen, W., Wang, N., … Ahmad, B.B. 2019. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Science of the Total Environment. doi: 10.1016/j.scitotenv.2019.01.329.
34.Hong, H., Naghibi, S.A., Pourghasemi, H.R., and Pradhan, B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arab. J. Geosci. 9 (2): 1-26.
35.Hong, H., Pourghasemi, H.R., and Pourtaghi, Z.S. 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.
36.Hu, J., Zhu, H., Mao, Y., Zhang, C., Liang, T., and Mao, D. 2019. Using uncertain DM chameleon clustering algorithm based on machine learning to predict landslide jazards. J. Robot. Mechatron. 31 (2): 329-338.
37.Kayastha, P., Dhital, M.R., and De Smedt, F. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat. Hazards. 63: 479-498.
38.Koca, T.K., and Koca, M.Y. 2019. Volume estimation and evaluation of rotational landslides using multi-temporal aerial photographs in Çaglayan dam reservoir area, Turkey. Arabian J. Geosci. 12 (5): 140.
39.Komac, M. 2006. A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia, Geomorphology, 74: 17-28.
40.Kornejady, A., Ownegh, M., and Bahremand, A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152: 144–162. doi: 10.1016/j.catena.
41.Leventhal, A.R., and Kotze, G.P. 2008. Landslide susceptibility and hazard mapping in Australia for land-use planning - with reference to challenges in metropolitan suburbia. Eng. Geol.102 (3-4): 238-250.
42.Liu, J., et al. 2004. Landslide hazard assessment in the Three Gorges area of the Yangtze River using ASTER imagery: Zigui–Badong. Geomorphology. 61: 171-187.
43.Liu, Y., Guo, Q., and Tian, Y. 2012.A software framework for classification models of geo graphical data. Comput. Geosci. 42: 47-56.
44.Lopez, R.P., Larrea-Alcazar, D., and Zenteno-Ruiz, F. 2011. Spatial pattern analysis of dominant species inthe Prepuna: Gaining insight into community dynamics in the semi-arid, subtropical Andes. J. Arid Environ.74: 1534-1539.
45.Lumb, P. 1975. Slope failure in Hong Kong. Q. 1. Eng. Geol. 8: 31-65.
46.McCarthy, E., Moretti, D., Thomas, L., DiMarzio, N., Morrissey, R., Jarvis, S., ... Dilley, A. 2011. Changes in spatial and temporal distribution and vocal behavior of Blainville's beakedwhales (Mesoplodon densirostris) during multiship exercises with mid-frequency sonar. Mar. Mamm. Sci. 27 (3): 206-226.
47.Meinhardt, M., Fink, M., and Tunschel, 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.
48.Michele, Ch., and Kang-tsung, 2012. Landslide inventory maps: new tools for an old problem. Earth-Sci. Rev. 112 (1-2): 42-66. https://doi.org/ 10.1016/j.earscirev. 2012.02.001. 49.Moreno, R., Zamora, R., Molina, J.R., Vasquez, A., and Herrera, M.Á. 2011. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using maximum entropy (Maxent). Ecological Informatics, 6(6): 364-370.
50.Mundalik, V., Fernandes, C., Kadam, A.K., and Umrikar, B.N. 2018. Integrated geomorphological, geospatial and AHP technique for groundwater prospects mapping in basaltic terrain. Hydro spatial Analysis, 2 (1), 16-27.
51.Nasiri Aghdam, I., Varzandeh, M.H.M., and Pradhan, B. 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ. Earth Sci. 75: 1–20.
52.Nefeslioglu, H.A., Duman, T.Y.,and Durmaz, S. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94: 401-418.
53.Pandey, V.K., Sharma, K.K., Pourghasemi, H.R., and Bandooni, S.K. 2019. Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India. CATENA, 182: 104150. doi: 10.1016/j.catena.2019.104150.
54.Pandey, V.K., Pourghasemi, H.R.,and Sharma, M.C. 2018. Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya. Geocarto Int.pp. 1-20.
55.Park, N.W. 2015. Using maximum entropy modeling for landslide susceptibility mapping with multiple geo environmental data sets. Environmental Earth Sciences, 73(3): 937-949.
56.Park, N.W., and Chi, K.H. 2008. Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. Int. J. Remote Sens.29: 247-264.
57.Peterson, A.T. 2006. Uses and Requirements of Ecological Niche Models and Related Distributional Models.
58.Pham, B.T., Bui, D.T., Prakash, I., and Dholakia, M. 2016. Rotation Forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat. Hazards. 83: 97-127.
59.Pham, B.T., et al. 2017. A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomat. Nat. Haz. Risk. 8: 649-671.
60.Phillips, S.J., Anderson, R.P., and Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4): 231-259.
61.Pourghasemi, H.R., and Rahmati, O. 2018. Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA, 162: 177-192. doi: 10.1016/j.catena.2017.11.022.
62.Pourghasemi, H.R., Gayen, A., Panahi, M., Rezaie, F., and Blaschke, T. 2019. Multi-hazard probability assessment and mapping with emphasis on landslides, floods, and earthquakes in Iran. Sci. Total Environ. https://doi.org/10.1016/j. scitotenv.2019.07.203.
63.Pourghasemi, H.R., and Kerle, N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. Earth Sci.75: 1-17.
64.Pourghasemi, H.R., Moradi, H.R., Fatemi Aghda, S.M., Gokceoglu, C., and Pradhan, B. 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab. J. Geosci. 7: 1857-1878.
65.Pourghasemi, H.R., Yousefi, S., Kornejady, A., and Cerda, A. 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609: 764-775.
66.Pradhan, B. 2010. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J. Indian Soc. Remote Sens. 38(2): 301-320.
67.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.
68.Rabby, Y.W., and Li, Y. 2019. An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping. Landslides, 16 (3): 633-645.
69.Rahmati, O., Kornejady, A., Samadi, M., Deo, R.C., Conoscenti, C., Lombardo, L., Dayal, K., Taghizadeh-Mehrjardi, R., Pourghasemi, H.R., Kumar, S., and Tien Bui, D. 2019a. PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches. Science of the Total Environment,664: 296-311. https://doi. org/10.1016/ j.scitotenv.2019.02.017.
70.Rajasekhar, M., Sudarsana Raju, G., Sreenivasulu, Y., and Siddi Raju, R. 2019. Delineation of groundwater potential zones in semi-arid region of Jilledubanderu river basin, Anantapur District, Andhra Pradesh, India using fuzzy logic, AHP and integrated fuzzy-AHP approaches. Hydro Research. doi: 10.1016/j.hydres.2019.11.006.
71.Regmi, A.D., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Khumamoto, T., and Akgun, A. 2014. Application of frequency ratio, statistical index and weights-of-evidence models, and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. ArabJ Geosci. 7(2): 725-742. doi:10.1007/s12517-012-0807-z.
72.Remondo, J., González, A., Díazde Terán, J.R., Cendrero, A., Fabbri, A., and Chung, C.J.F. 2003. Validation of landslide susceptibility maps; examples and applications from a casestudy in Northern Spain. Nat. Hazds. 30(3): 437-449. doi:10.1023/B: NHAZ. 0000007201. 80743.fc.
73.Ruddell, B.L., Brunsell, N.A., and Stoy, P. 2013. Applying Information Theory in the Geosciences to quantify process uncertainty, feedback, Scale. Eps.94: 56-57.
74.Saaty, T.L. 1990. Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS publications.
75.Shahabi, H., Hashim, M., and Ahmad, B.B. 2015. Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environ. Earth Sci. 73: 1-22.
76.Shahabi, H., Khezri, S., Ahmad, B.B., and 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: 55-70.
77.Shirzadi, A., et al. 2017. Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environ. Earth Sci. 76: 152.
78.Shirzadi, A., Solaimani, K., Roshan, M.H., Kavian, A., Chapi, K., Shahabi, H., Keesstra, S., Ahmad, B.B., and Bui, D.T. 2011. Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution. Catena. 178: 172-188.
79.Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B., and Revhaug, I. 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression,and logistic model tree. Landslides.13: 361-378.
80.Trigila, A., Iadanza, C., Esposito, C., and Scarascia-Mugnozza, G. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy), Geomorphology, 249: 119-136.
81.Tsangaratos, P., and Ilia, I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides. 13: 305-320.
82.Tsangaratos, P., Ilia, I., Hong, H., Chen, W., and Xu, C. 2017. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14: 1091-1111.
83.Vapnik, V.N., and Vapnik, V. 1998. Statistical Learning Theory. Wiley,New York.
84.Wang, Q., Li, W., Chen, W., and Bai, H. 2015. GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J. Earth Syst. Sci. 124 (7): 1399-1415.
85.Wang, Y., Fang, Z., and Hong, H. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Science of the Total Environment. doi: 10.1016/j.scitotenv.2019.02.263.
86.Yalcin, A., Reis, S., Aydinoglu, A.C., and Yomralioglu, T. 2011. AGIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85 (3): 274-287.
87.Yang, X.Q., Kushwaha, S.P.S., Saran, S., Xu, J., and Roy, P.S. 2013. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51: 83-87.
88.Yesilnacar, E.K. 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey (PhD Thesis). Department of Geomatics the University of Melbourne, 423p.
89.Yilmaz, I. 2010. The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural network (ANN). Environ. Earth Sci.60: 505-519.
90.Yost, A.C., Petersen, S.L., Gregg, M., and Miller, R. 2008. Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy anda long-term dataset from Southern Oregon. Ecological Informatics,3(6): 375-386.
91.Youssef, A.M., Al-Kathery, M., and Pradhan, B. 2014. Landslide susceptibility mapping at AlHasher Area, Jizan (Saudi Arabia) usingGIS-based frequency ratio and index of entropy models. Geosci J. doi:10.1007/s12303-014-0032-8.
92.Youssef, A.M., Pourghasemi, H.R., Pourtaghi, Z.S., and Al-Katheeri, M.M. 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.
93.Youssef, A.M., Pradhan, B., Jebur, M.N., and El-Harbi, H.M. 2015. Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area,Saudi Arabia. Environ. Earth Sci.73: 3745-3761.
94.Zhao, C., Chen, W., Wang, Q., Wu, Y., and Yang, B. 2015. A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China. Arab. J. Geosci. 8 (11): 1-10.
95.Zhao, Y., Huang, Y., Liu, H., Wei, Y., Lin, Q., and Lu, Y. 2018. Use of the normalized difference road landside index (NDRLI)-based method for the quick delineation of road-induced landslides. Sci. Rep. 8 (1): 17815.
96.Zipkin, E.F., Grant, E.H.C., and Fagan, W.F. 2012. Evaluating the predictive abilities of community occupancy models using AUC while accountingfor imperfect detection. Ecological Applications, 22(7): 1962-1972. | ||
|
آمار تعداد مشاهده مقاله: 712 تعداد دریافت فایل اصل مقاله: 473 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب