• صفحه اصلی
  • مقایسه رویه های شبکه عصبی مصنوعی، رگرسیون لجستیک و یادگیری برمبنای نمونه وزنی مشابهت در مدل سازی و پیش بینی جنگل زدایی مطالعه موردی: حوزه آبخیز گرگانرود- استان گلستان

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-1610-3028 (R2) بازدید : 145 صفحه: 217 - 227

10.22034/jest.2020.10480

نوع مقاله: پژوهشی

مقایسه رویه های شبکه عصبی مصنوعی، رگرسیون لجستیک و یادگیری برمبنای نمونه وزنی مشابهت در مدل سازی و پیش بینی جنگل زدایی مطالعه موردی: حوزه آبخیز گرگانرود- استان گلستان

محورهای موضوعی : سیستم اطلاعات جغرافیایی

زینب مرادی 1 , علیرضا میکاییلی تبریزی 2

1 - دانش آموخته کارشناسی ارشد محیط زیست، گروه محیط‌زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.
2 - دانشیار، گروه محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران * (مسوول مکاتبات).

تاریخ دریافت : 1395/07/11 تاریخ پذیرش : 1395/12/25 تاریخ انتشار : 1398/11/01

کلید واژه: شبکه عصبی مصنوعی, یادگیری بر مبنای نمونه وزنی مشابهت, حوزه آبخیز گرگانرود, رگرسیون لجستیگ, جنگل‌زدایی,

چکیده مقاله :

زمینه و هدف: تغییر در پوشش جنگلی در خدمات اکوسیستمی، تعادل کربن در جو و در نتیجه تغییرات آب و هوا نقش بسیار مهمی ایفا می کند. هدف از این تحقیق مقایسه سه روش شبکه عصبی مصنوعی، رگرسیون لجستیک و یادگیری برمبنای نمونه وزنی مشابهت، جهت پیش بینی روند مکانی تغییرات پوشش جنگل است. روش بررسی: در این مطالعه از نقشه های کاربری اراضی تولید شده از ماهواره Landsat سنجنده TM مربوط به سال های 1984 و 2012 استفاده شد. مدل سازی پتانسیل انتقال با استفاده از شبکه عصبی مصنوعی، رگرسیون لجستیک و یادگیری برمبنای نمونه وزنی مشابهت و پیش بینی تغییرات برای بهترین مدل با استفاده از زنجیره مارکف انجام شد. به منظور برآورد صحت مدل سازی از آماره های ROC، نسبت موفقیت به هشدار خطا و عدد شایستگی استفاده شد. یافته ها: نتایج بیان گر صحت بالای شبکه عصبی مصنوعی با میزان ROC برابر 975/0 ، نسبت موفقیت به هشدار خطا 63 درصد و عدد شایستگی 12 درصد می باشد. بحث و نتیجه گیری: شبکه های عصبی مصنوعی در مقایسه با رگرسیون لجستیک و یادگیری بر مبنای نمونه وزنی مشابهت از صحت بالاتر و خطای کم تری در مدل سازی و پیش بینی تغییرات جنگل برخوردارند.

چکیده انگلیسی:

Background and Objective: The change in forest cover plays a vital role in ecosystem services, atmospheric carbon balance and thus climate change. The goal of this study is comparison of three procedure of Artificial Neural Network, Logistic regression and Similarity weighted Instance-based Learning (SIM Weight) to predict spatial trend of forest cover change. Method: In this study, land use maps for the periods 1984 and 2012 derived from Landsat TM satellite imagery, was used. Transition potential modeling using artificial neural network, Logistic regression and Similarity weighted Instance-based Learning and prediction based on the best model using Markov chain model was performed. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. Findings: The results show the accuracy of artificial neural network with the ROC equal to 0.975, the ratio Hits/False Alarms equal to 63 percent and the figure of merit is equal to 12 percent. Discussion and Conclusions: Artificial Neural Networks in comparison with Logistic Regression and Similarity weighted Instance-based Learning has higher accuracy and less error in modeling and predicting of forest changes.

منابع و مأخذ:


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_||_

  1. Le Quéré, C., Raupach, M.R., Canadell, J.G., Marland, G., Bopp, L., Ciais, P., Conway, T.J., Doney, S.C., Feely, R.A., Foster, P., Friedlingstein, P., Gurney, K., Houghton, R.A., House, J.I., Huntingford, C., Levy, P.E., Lomas, M.R., Majkut, J., Metzl, N., Ometto, J.P., Peters, G.P., Prentice, I.C., Randerson, J.T., Running, S.W., Sarmiento, J.L., Schuster, U., Sitch, S., Takahashi, T., Viovy, N., van der Werf, G.R., Woodward, F.I., 2009. Trends in the sources and sinks of carbon dioxide. Nat. Geosci. 2, 831–836.
    2.
  2. Van der Werf, G.R., Morton, D.C., DeFries, R.S., Olivier, J.G.J., Kasibhatla, P.S., Jackson, R.B., Collatz, G.J., Randerson, J.T., 2009. CO2 emissions from forest loss. Nat. Geosci. 2, 737–738.
    3.
  3. Darvishsefat, A & Namiranian, M. (2004). The study of spatial distribution of changes in the northern forests of  Iran. http://www.GIS Development. nat/application/nrm/overview P: 1-2.
    4.
  4. Nadali, Azadeh ; Mahini, Abdorrasoul ; Feghhi, Jahangir ; Riyazi, Borhan. 2012. Tree Cover detection through Maxlike Classification of Land sat ETM + Images of the Year 2001 in Golestan Province. Journal of Environmental Sciences and Technology, 2012 (Issue 3, Year 14). PP. 47-56. (In Persian)
    5.
  5. Mirakhorlou, Khosro and Akhavan, Reza. 2008. Investigation on boundary changes of northern forests of Iran using remotely sensed data. Iranian Journal of Forest and Poplar Research, 2008(Issue 1. Year 16). PP. 139-148. (In Persian)
    6.
  6. Kushwaha, S.P.S., 1990. Forest type mapping and change detection from satellite imagery. ISPRS J. Photogram. Remote Sens. 45, 175–181.
    7.
  7. FSI, 2011. India State of Forest Report 2011. Forest Survey of India, Ministry of Environment and Forests, Dehradun.
    8.
  8. Srivastava, S., Singh, T.P., Singh, H., Kushwaha, S.P.S., Roy, P.S., 2002. Mapping of large-scale deforestation in Sonitpur district, Assam. Curr. Sci. 82 (12), 1479–1484.
    9.
  9. Nandy, S., Kushwaha, S.P.S., Mukhopadhyay, S., 2007. Monitoring Chilla-Motichur corridor using geospatial tools. J. Nat. Conserv. 15 (4), 237–244.
    10.
  10. Kushwaha, S.P.S., Hazarika, R., 2004. Assessment of habitat loss in Kameng and Sonitpur Elephant Reserves. Curr. Sci. 87 (10), 1447–1453.
    11.
  11. Mahini, A. R., Turner, B. J. 2003. Modeling Past Vegetation Change Through Remote Sensing and G.I.S: A Comparison of Neural Networks and Logistic Regression Methods. School of Resources, Environment and Society. The Australian National. University, Canberra 0200, Australia.
    12.
  12. Mas, J. F., Puig, H., Palacio, J. L., Lopez, A. S. 2004. Modeling deforestation using GIS and artificial neural networks. Environmental Model Software, 19, 461–471.
    13.
  13. Khoi, D., Y. Murayama. 2011. Modeling Deforestation Using a Neural Network-Maarkov Model. Spatial Analysis and Modeling in Geographical Transformation Process. GeoJournal Library 100:169-190.
    14.
  14. Kumar R., Nandy S., Agarwal R., Kushwaha S. P. S., 2014. Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45: 444-455.
    15.
  15. Bagheri, Reza and Shataii, Shaban. 2010. Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province). Iranian Journal of Forest, 2010(Issue 3. Year 2). PP. 243-252. (In Persian).
    16.
  16. Hasan Zadeh Poshtemsari, M. 2011. Modeling deforestation with multivariate statistical methods and artificial neural network in Fallard forest. M.Sc. Thesis. Faculty of Natural Resources. University of Shahre-Kord. (In Persian).
    17.
  17. Arekhi, Saleh; Aliakbar Jafarzadeh; Saleh Yousefi. 2012. Modeling Deforestation Using Logistic Regression, GIS and RS Case study: Northern Forests of the Ilam Province. Geography and Development Iranian Journal , 2012(Issue 29. Year 10). PP. 31-42. (In Persian)
    18.
  18. Sardar Zade, A.; Matkan, A.A. ; Sadati Nejad, S.J. ; Ashourlou, D. 2012. Forests demolition prediction using RS & GIS techniques and integration of Artificial Neural Network-Markov chain (Chehelchay basin, Golestan province). 20# National Conf. of Map and Geomatic (conf.ncc.org.ir). Iran.Tehran. (In Persian)
    19.
  19. Gholamalifard, Mehdi ; Jourabian Shooshtari, SHarif ; Abkar, Ali Akbar ; Naimi, Babak. 2014. Comparison of Logistic Regression and Artificial Neural Network Algorithms in Land Cover Transition Potential Empirical Modeling of Coastal Areas of Mazandaran Province. Environmental Researches, 2014(Issue 9. Year 5). PP. 167-176. (In Persian)
    20.
  20. KhakPour, A., Mehrdadi, N., Nouri, R.H., Soroush, M., 2009. Assessment of the quality of the Gorgan-roud River based on field studies. Third Professional Conference on Environmental Engineering. Faculty of Environment – Univ. of Tehran. Tehran. (In Persian)
    21.
  21. Anonymous. 2012. Landuse Plan of Golestan Province. Deputy Director General of Golestan Governorate. (In Persian)
    22.
  22. Haibo, Y., Longjiang, D., Hengliang, G., Jie, Zh. 2011. Tai'an Land Use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences, 10(C): 2625-2630.
    23.
  23. Kim O. S., 2010. An assessment of deforestation models for reducing emissions from deforestation and forest degradation (REDD). Transactions in GIS, 14 (5): 631-654.
    24.
  24. Eastman, J.R., 2012. IDRISI Help System. Accessed in IDRISI Selva 17.02. Clark Labs, Clark University, Worcester, MA.
    25.
  25. Pontius, R. G. and L. C. Schneider .2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment 85(1): 239-248.
    26.
  26. Ajami, Mohammad and Khormali, Farhad., 2012. Pedogenic and Micromorphological Evidences of Land Degradation on Deforested Loess-Derived Soils in Eastern Golestan Province. ,Journal of Sci. & Technology in Agriculture And Natural Res. -  Water & Soil Sci. 2013(Issue 3/61. Year 16). Iran. PP. 141-153. (In Persian).
    27.
  27. Eastman, J. R. 2006. IDRISI Andes, Guide to GIS and Image Processing, Clark Labs, Clark University, Worcester, MA.
    28.
  28. Schaap MG, Bouten W. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resource Res. 32:3033–3040.
    29.

 

 


  1. Le Quéré, C., Raupach, M.R., Canadell, J.G., Marland, G., Bopp, L., Ciais, P., Conway, T.J., Doney, S.C., Feely, R.A., Foster, P., Friedlingstein, P., Gurney, K., Houghton, R.A., House, J.I., Huntingford, C., Levy, P.E., Lomas, M.R., Majkut, J., Metzl, N., Ometto, J.P., Peters, G.P., Prentice, I.C., Randerson, J.T., Running, S.W., Sarmiento, J.L., Schuster, U., Sitch, S., Takahashi, T., Viovy, N., van der Werf, G.R., Woodward, F.I., 2009. Trends in the sources and sinks of carbon dioxide. Nat. Geosci. 2, 831–836.
    2.
  2. Van der Werf, G.R., Morton, D.C., DeFries, R.S., Olivier, J.G.J., Kasibhatla, P.S., Jackson, R.B., Collatz, G.J., Randerson, J.T., 2009. CO2 emissions from forest loss. Nat. Geosci. 2, 737–738.
    3.
  3. Darvishsefat, A & Namiranian, M. (2004). The study of spatial distribution of changes in the northern forests of  Iran. http://www.GIS Development. nat/application/nrm/overview P: 1-2.
    4.
  4. Nadali, Azadeh ; Mahini, Abdorrasoul ; Feghhi, Jahangir ; Riyazi, Borhan. 2012. Tree Cover detection through Maxlike Classification of Land sat ETM + Images of the Year 2001 in Golestan Province. Journal of Environmental Sciences and Technology, 2012 (Issue 3, Year 14). PP. 47-56. (In Persian)
    5.
  5. Mirakhorlou, Khosro and Akhavan, Reza. 2008. Investigation on boundary changes of northern forests of Iran using remotely sensed data. Iranian Journal of Forest and Poplar Research, 2008(Issue 1. Year 16). PP. 139-148. (In Persian)
    6.
  6. Kushwaha, S.P.S., 1990. Forest type mapping and change detection from satellite imagery. ISPRS J. Photogram. Remote Sens. 45, 175–181.
    7.
  7. FSI, 2011. India State of Forest Report 2011. Forest Survey of India, Ministry of Environment and Forests, Dehradun.
    8.
  8. Srivastava, S., Singh, T.P., Singh, H., Kushwaha, S.P.S., Roy, P.S., 2002. Mapping of large-scale deforestation in Sonitpur district, Assam. Curr. Sci. 82 (12), 1479–1484.
    9.
  9. Nandy, S., Kushwaha, S.P.S., Mukhopadhyay, S., 2007. Monitoring Chilla-Motichur corridor using geospatial tools. J. Nat. Conserv. 15 (4), 237–244.
    10.
  10. Kushwaha, S.P.S., Hazarika, R., 2004. Assessment of habitat loss in Kameng and Sonitpur Elephant Reserves. Curr. Sci. 87 (10), 1447–1453.
    11.
  11. Mahini, A. R., Turner, B. J. 2003. Modeling Past Vegetation Change Through Remote Sensing and G.I.S: A Comparison of Neural Networks and Logistic Regression Methods. School of Resources, Environment and Society. The Australian National. University, Canberra 0200, Australia.
    12.
  12. Mas, J. F., Puig, H., Palacio, J. L., Lopez, A. S. 2004. Modeling deforestation using GIS and artificial neural networks. Environmental Model Software, 19, 461–471.
    13.
  13. Khoi, D., Y. Murayama. 2011. Modeling Deforestation Using a Neural Network-Maarkov Model. Spatial Analysis and Modeling in Geographical Transformation Process. GeoJournal Library 100:169-190.
    14.
  14. Kumar R., Nandy S., Agarwal R., Kushwaha S. P. S., 2014. Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45: 444-455.
    15.
  15. Bagheri, Reza and Shataii, Shaban. 2010. Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province). Iranian Journal of Forest, 2010(Issue 3. Year 2). PP. 243-252. (In Persian).
    16.
  16. Hasan Zadeh Poshtemsari, M. 2011. Modeling deforestation with multivariate statistical methods and artificial neural network in Fallard forest. M.Sc. Thesis. Faculty of Natural Resources. University of Shahre-Kord. (In Persian).
    17.
  17. Arekhi, Saleh; Aliakbar Jafarzadeh; Saleh Yousefi. 2012. Modeling Deforestation Using Logistic Regression, GIS and RS Case study: Northern Forests of the Ilam Province. Geography and Development Iranian Journal , 2012(Issue 29. Year 10). PP. 31-42. (In Persian)
    18.
  18. Sardar Zade, A.; Matkan, A.A. ; Sadati Nejad, S.J. ; Ashourlou, D. 2012. Forests demolition prediction using RS & GIS techniques and integration of Artificial Neural Network-Markov chain (Chehelchay basin, Golestan province). 20# National Conf. of Map and Geomatic (conf.ncc.org.ir). Iran.Tehran. (In Persian)
    19.
  19. Gholamalifard, Mehdi ; Jourabian Shooshtari, SHarif ; Abkar, Ali Akbar ; Naimi, Babak. 2014. Comparison of Logistic Regression and Artificial Neural Network Algorithms in Land Cover Transition Potential Empirical Modeling of Coastal Areas of Mazandaran Province. Environmental Researches, 2014(Issue 9. Year 5). PP. 167-176. (In Persian)
    20.
  20. KhakPour, A., Mehrdadi, N., Nouri, R.H., Soroush, M., 2009. Assessment of the quality of the Gorgan-roud River based on field studies. Third Professional Conference on Environmental Engineering. Faculty of Environment – Univ. of Tehran. Tehran. (In Persian)
    21.
  21. Anonymous. 2012. Landuse Plan of Golestan Province. Deputy Director General of Golestan Governorate. (In Persian)
    22.
  22. Haibo, Y., Longjiang, D., Hengliang, G., Jie, Zh. 2011. Tai'an Land Use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences, 10(C): 2625-2630.
    23.
  23. Kim O. S., 2010. An assessment of deforestation models for reducing emissions from deforestation and forest degradation (REDD). Transactions in GIS, 14 (5): 631-654.
    24.
  24. Eastman, J.R., 2012. IDRISI Help System. Accessed in IDRISI Selva 17.02. Clark Labs, Clark University, Worcester, MA.
    25.
  25. Pontius, R. G. and L. C. Schneider .2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment 85(1): 239-248.
    26.
  26. Ajami, Mohammad and Khormali, Farhad., 2012. Pedogenic and Micromorphological Evidences of Land Degradation on Deforested Loess-Derived Soils in Eastern Golestan Province. ,Journal of Sci. & Technology in Agriculture And Natural Res. -  Water & Soil Sci. 2013(Issue 3/61. Year 16). Iran. PP. 141-153. (In Persian).
    27.
  27. Eastman, J. R. 2006. IDRISI Andes, Guide to GIS and Image Processing, Clark Labs, Clark University, Worcester, MA.
    28.
  28. Schaap MG, Bouten W. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resource Res. 32:3033–3040.
    29.

 

 

 
 



 
 



 

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