ارزیابی مدل‌های مختلف داده‌کاوی برای پیش‌بینی نقشه حساسیت فرسایش آبکندی در حوزه آبخیز رباط ترک استان مرکزی

نوع مقاله : علمی -پژوهشی

نویسندگان

1 گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس تهران، تهران، ایران

2 گروه برنامه‌ریزی، مدیریت و آموزش محیط زیست، دانشکده محیط زیست دانشگاه تهران، تهران، ایران

3 گروه مرتع و آبخیزداری، دانشکده علوم کشاورزی و منابع طبیعی دانشگاه جیرفت، جیرفت، ایران

چکیده

فرسایش آبکندی یکی از اشکال فرسایشی است که موجب هدر رفت مقدار زیادی خاک می­گردد. بنابرین از این فرسایش می­توان به ­عنوان یکی از علل اصلی تخریب زمین و محیط زیست نام برد. این تحقیق با هدف پهنه­بندی حساسیت فرسایش آبکندی با استفاده از مدل­های داده­کاوی، مدل خطی تعمیم یافته (GLM) و شبکه عصبی مصنوعی (ANN) در حوزه آبخیز رباط ترک انجام شد. مناطق دارای فرسایش آبکندی طی بازدیدهای میدانی شناسایی و تعداد 242 نقطه فرسایشی انتخاب گردید. 12 متغیر محیطی موثر در فرسایش آبکندی، نقشه رقومی ارتفاع، درجه شیب، جهت شیب، شکل شیب، شاخص همگرایی، فاصله از رودخانه، تراکم زهکشی، فاصله از جاده، سنگ­شناسی، کاربری اراضی، شاخص اختلاف پوشش گیاهی نرمال شده (NDVI) و نقشه هم باران به منظور مدل­سازی حساسیت فرسایش آبکندی مورد استفاده قرار گرفتند. به منظور ارزیابی و اعتبارسنجی مدل­های مورد استفاده از معیارهای ROC، TSS و Kappa استفاده شد. نتایج حاصل از ارزیابی مدل نشان داد که مدل­ GLM با مقدار ROC، Kappa و TSS به ترتیب 89/0، 7/0 و 7/0 و مدل ANN با ROC، Kappa و TSS به ترتیب 88/0، 7/0 و 7/0 کارایی خیلی خوبی در مدل­سازی مناطق حساس به فرسایش آبکندی دارند. همچنین بررسی کلی مدل­های مورد استفاده براساس شاخص­های ذکر شده نشان داد که مدل GLM دارای کارایی مناسب­تری نسبت به مدل ANN در منطقه مورد مطالعه دارد. نتایج حاصل از پهنه­بندی حساسیت فرسایش آبکندی در منطقه مورد مطالعه نشان داد که مناطق مرکزی حوزه دارای حساسیت خیلی زیاد و زیاد نسبت به فرسایش آبکندی می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluating the Capability of Various Types of Geomorphological facies in supplying dust sources in the west of Khozestan province- Iran

نویسندگان [English]

  • ُSaeid Janizadeh 1
  • َAhmad Nohegar 2
  • Mohammadtaghi Avand 1
  • Mojtaba Dolat Kordestani 3
1 Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University of Tehran, Tehran, Iran
2 Tehran university
3 Department of Combat desertification, Faculty of Range and Watershed, Jiroft university, Jiroft, Iran.
چکیده [English]

Soil erosion is a problem for agriculture in arid and semi-arid regions and is of great importance due to its long-term effects on soil fertility and sustainable agriculture. Among the types of water erosion, gully erosion is one of the most important events in soil erosion and land reclamation. Given that the Markazi province is located in a region with arid and semi-arid climate, the intensity of rainfall is high in some months of the year. Also because of the abandoned agricultural land in the study area, there is much vegetation exposed to severe erosion which is conducive to erosion such as gutter erosion, so serious attention is needed for this area. The data mining method extracts useful information from a large volume of data and has shown good performance based on the literature review. Therefore, the aim of the present study was to prioritize environmental factors affecting the occurrence of gully erosion with data mining and statistical methods.Material and MethodsIn order to conduct the present study and to map the distribution of gully erosion zones in the Robat Turk watershed, 242 gully data were identified in the study area and used. A total of 242 points were identified as non-flooded areas. In order to model the data, it was divided into two categories of training and validation, with 70% of data used as training and 30% of data used as validation. Based on the research background, hydrological, geological and physiographic factors including elevation, slope, aspect, curvature, slope shape, distance from river, distance from road, lithology, land use, annual precipitation and NDVI, variables were selected for modeling. In order to model the gully erosion, artificial neural network (ANN) and generalize linear model (GLM) models were used, and the ROC and Kappa, TSS coefficient were used to determine the accuracy of the gully erosion susceptibility map.Result and discussion The results of gully erosion susceptibility showed that the central areas of the watershed are highly sensitive to erosion. Considering that most of the lands in the central part of the watershed are bare land and agricultural, the study of the gully erosion susceptibility map showed that the most sensitive and highly sensitive erosion susceptibility area was formed in the bare land. In relation to the influence of different elevation and slope classes in the study area on susceptibility to erosion, it should be stated that altitude class of 1800-2000 meters and slope class of 0-12% had the highest contribution to erosion susceptibility in the study area. This may be due to the higher soil compactness of these classes than other classes, which increases the likelihood of water infiltration into the soil and the possibility of material dissolution and piping. Validation results showed that GLM and ANN with ROC of 0.89 and 0.88 have very good performance regarding gully erosion susceptibility in the study area.ConclusionGully erosion is one of the erosion processes that widely affects the appearance of the earth. In this study, GLM and ANN were used to evaluate the impact of environmental variables on gully erosion as well as to identify potential areas for gully erosion. For this purpose, 12 variables and 242 gully erosion points were used. ROC, TSS and KAPPA statistics were used to evaluate the models. The results of evaluation and validation of the models used showed that both models have good performance in zoning susceptibility to gully erosion. Identification and prediction of gully erosion susceptible areas can reduce the damaging effects of this type of erosion and prevent its further development and can be of considerable help to the people of the study area. Given that most of the gutters were created in the central part of the study area near the village of Robat Turk, protective measures should be increased in these areas to prevent the spread of agriculture and residential areas to erosive areas. 

کلیدواژه‌ها [English]

  • Gully erosion
  • Data mining models
  • ROC curve
  • Rabat Turk watershed
-اکبری، م.، بشیری، م. و رنگ‌آور، ع.ا.، 1396. کاربرد الگوریتم‌های داده‌کاوی در تحلیل حساسیت و پهنه-بندی مناطق مستعد به فرسایش آبکندی در حوضه-های شاخص استان خراسان رضوی، پژوهش‌های فرسایش محیطی، شماره7: 2 (26)، ص 16-42.
-حسین‌زاده، م.م.، نصرتی، ک.، خلجی، س. و درفشی، خ.، 1395. گسترش فرسایش خندقی و طبقه‌بندی آن در حوضه آبخیز رباط ترک دلیجان، پژوهش‌های ژئومورفولوژی کمی، سال 5، شماره 2، ص 141-160.
-رضایی مقدم، م.ح.، نیکجو، م.ر.، ولی زاده، خ.، بلواسی، ا.ع. و بلواسی، م.، 1396. کاربرد مدل شبکه عصبی مصنوعی در پهنه‌بندی خطر زمین لغزش، نشریه جغرافیا و برنامه‌ریزی، سال 21، شماره 59، ص 89-111.
-عرب‌عامری، ع.، رضایی، خ. و یمانی، م.، 1397. تحلیل متغیرهای محیطی به منظور تهیه نقشه حساسیت فرسایش آبکندی در حوضه طرود با روش شواهد وزن قطعی، مجله مرتع و آبخیزداری، دوره 71، شماره 1، ص 97-114.
-شیرزادی، ع.، سلیمانی، ک.، حبیب‌نژاد، م.، کاویان، ع. و چپی، ک.، 1395. معرفی یک مدل جدید ترکیبی الگوریتم مبنا به منظور پیش‌بینی حساسیت زمین لغزش‌های سطحی اطراف شهر بیجار، جغرافیا و توسعه، شماره 46، ص 225-246.
-Angileri, S.E., Conoscenti, C. and Hochschild, V., 2016. Water erosion susceptibility mapping by applying stochastic gradient treeboost to the imera Meridionale River basin (Sicily, Italy): Geomorphology, DOI: 10.1016/j.geomorph.2016.03.018 .
-Avand, M., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Khosrobeigi Bozchaloei, S. and Blaschke, T., 2019. A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping: Water, v. 11, p. 2076.
-Arabameri, A., Rezaei, K., Pourghasemi, H.R., Lee, S. and Yamani, M., 2018. GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique: Environal Earth Science, v. 77, p. 1-22.
-Arabian Ameri, A., Rezaei, J. and Yamani, M., 2018. Analysis of environmental variables in terms of the sensitivity map of gully erosion in the Troud basin by means of definitive weight evidence: Journal of Range and Watershed Management, v. 71, p. 97- 114.
-Beullens, J., Van de Velde, D. and Nyssen, J., 2014. Impact of slope aspect on hydrological rainfall and on the magnitude of rill erosion in Belgium and northern France: Catena, v. 114, p. 129-139.
-Cama, M., Lombardo, L., Conoscenti, C. and Rotigliano, E., 2017. Improving transferability strategies for debris flow susceptibility assessmentApplication to the Saponara and Itala catchments (Messina, Italy): Geomorphology, v. 288, p. 52-65.
-Centeri, C.S., Herczeg, E., Vona, M., Balazc, K. and Penksza, K., 2009. The effect of land use change on plant-soil-erosion relations, Nyereg hill, Hungary: Journal of Plant Nutrition and Soil Science, v. 172, p. 586-592.
-Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V. and Märker, M., 2014. Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy): Geomorphology, v. 204, p. 399-411.
-Conoscenti, C., Agnesi, V., Angileri, S., Cappadonia, C., Rotigliano, E. and Marker, M., 2013. A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy: Environmental earth sciences, v. 70, p. 1179-1195.
-Dai, F.C., Lee, C.F. and Xu, Z.W., 2001. Assessment of landslide susceptibility on the natural terrain of Lantua sland, Hong Kong: Environment Geology, v. 40, p. 381-391
-Dayhoff, J.E., 1990. Neural Network Principles, Prentice-Hall International, U.S.A. 197 p.
-Dehnavi, A., Aghdam, I.N., Pradhan, B. and Varzandeh, M.H.M., 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran: Catena, v. 135, p. 122-148.
-Gabris, G.Y., Kertesz, Á. and Zambo, L., 2003. Land use change and gully formation over the last 200 years in a hilly catchment: Catena, v. 50, p. 151-164.
-Galang, M.A., Markewitz, D., Morris, L.A. and Busseli, P., 2007. Land use change and gully erosion in the Piedmont region of South Carolin: Journal of Soil and Water Conservation, v. 62, p. 122-129.
-Golkarian, A., Naghibi, S.A., Kalantar, B. and Pradhan, B., 2018. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS: Environmental monitoring and assessment, v.190, p. 149-167.
-Grace, J.M., 2004. Soil erosion following forest operations in the Southern Piedmont of central Alabama: Journal of Soil and Water Conservation, v. 59, p. 180-185.
-He, M.Z., Zheng, J.G., Li, X.R. and Qian, Y.L., 2007. Environmental factors affecting vegetation composition in the Alxa Plateau, China: Journal of Arid Environment, v. 69, p. 473-489.
-Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J. and Sattarian, A., 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran: Internatoinal Journal of Environmenal Science Technology, v. 11, p. 909-926.
-Janizadeh, S., Avand, M., Jaafari, A., Phong, T.V., Bayat, M., Ahmadisharaf, E. and Lee, S., 2019. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran: Sustainability, v. 11, p. 54-72.
-Khanna, T., 1990. Foundation of neural networks, Addison-Wesley Publishing Company, U.S.A., 327 p.
-Lee, S., Park, I. and Choi, J.K., 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network: Environment Management, v. 49, p. 347-358.
-Naghibi, S.A., Pourghasemi, H.R., Pourtaghi, Z.S. and Rezaei, A., 2014. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran: Earth Science Information.
-Pelletier, J.D., Barron-Gafford, G.A., Breshears, D.D., Brooks, P.D., Chorover, J., Durcik, M., Harman, C.J., Huxman, T.E., Lohse, K.A. and Lybrand, R., 2013. Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona: Journal of Geophysics Resource Earth Surface, v. 118, p. 741-758.
-Poesen, J., Vandekerckhove, L., Nachtergaele, J., Oostwoud Wijdenes, D., Verstraeten, G. and Wesemael, B., 2002. Gully erosion in dryland environments: Wiley, Chichester, UK, p. 229-262.
-Pourghasemi, H.R., Yousefi, S., Kornejady, A. and Cerdà, A., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling: Science of the Total Environment, v. 609, p. 764-775.
-Rahmati, O., Haghizadeh, A., Pourghasemi, H.R. and Noormohamadi, F., 2016. Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison: Natural Hazards, v. 82, p. 1231-1258.
-Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Reza, H. and Feizizadeh, B., 2017. Geomorphology Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion: Geomorphology, v. 298, p. 118-137.
-Shadfar, S., 2015. Application of Fuzzy Logic operators for investigation of gully erosion using GIS (Case Study: Troud basin): Journal of Geographical Information, v. 23, p. 35-42.
-Tehrany, M.S., Pradhan, B. and Jebur, M.N., 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS: Journal of Hydrology, v. 512, p. 332-343.
-Turner, H., 2008. Introduction to Generalized Linear Models, Department of Statistics University of Warwick, UK, 375 p.
-Wang, L., Wei, S., Horton, R. and Shao, M., 2011. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China: Catena, v. 87, p. 90-100.
-Water Resources Company of Markazi (WRCM), 2017. Precipitation and temperature reports. http://www.marw.ir/
-Yamani, S., Zamanzadeh, M. and Ahmadi, M., 2013. Analysis of factors affecting the formation and development of gully erosion: a case study of Kahoor plain in Fars Province: Geographical Exploration Desert, v. 1, p. 53-84.
-Yesilnacar, E.K., 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis Department of Geomatics the University of Melbourne, 423 p.
-Youssef, A.M., Pradhan, B. and Hassan, A.M., 2011. Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery: Environmental Earth Science, v. 62, p. 611-623.
-Zabihi, M., Mirchooli, F., Motevalli, A., Khaledi Darvishan, A., Pourghasemi, H.R., Zakeri, M.A. and Sadighi, F., 2018. Spatial modelling of gully erosion in Mazandaran Province, northern Iran: Catena, v. 161, p. 1-13.
-Zakerinejad, R. and Maerker, M., 2015. An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran: Natural Hazards, v. 79, p. 25-50.