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:: Volume 6, Issue 12 (12-2018) ::
PEC 2018, 6(12): 153-176 Back to browse issues page
Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands
Maryam Ahmadi Jolandan , Ghasemali Dianati Tilaki * , Vahid Gholami
Associate Professor, Department of Range Management, University of Tarbiat Modares , Dianatig@modares.ac.ir
Abstract:   (4354 Views)

Rangeland is one of the important natural resources in different aspects such as forage production, livestock, promenade and soil and water conservation. Therefore, it is necessary to study rangelands for their sustaible management and conservation. Since field studies of rangelands are time consuming  and costly, it was common to apply models aimed at estimating rangelands vegetation parameters. In this study, ANN was used to estimate rangelands cover percent and GIS was used as a pre-processing and post-processing in modeling respectively in Baladeh rangelands in Mazandaran Province. Multi-layer percepetron (MLP) network and multivariate regression method were used to estimate rangelands cover percent (training stage). In modeling process, sampling and estimation cover percent was also performed  in the 127 sites. Also, the affecting factors in cover percent such as topography, climatic factors, soil and mankind factors were evaluated.  Multivariate regression and stepwise method were used to simulate rangeland cover in SPSS software. Using cover percent as desired parameter and the affecting factors in cover percent as the network inputs,  an optimal network was presented. Then, optimum network was verified (test stage). The study area was divided with the pixels 1×1 km (raster format) in GIS medium. Then, the model input layers were coupled and a raster layer which included the model inputs values and geographic coordinate was generated. The values of pixels (model inputs) were entered into ANN with geographic coordinate. The results showed that ANN has a higher efficiency and accuracy (model test; Rsqr=0.72) than multivariate regression method (model test; Rsqr=0.6)  in rangeland cover modeling. In the next step, cover percent was simulated using the verified optimum network for all study rangelands. Finally, the results of ANN simulation were entered into GIS and cover percent map was generated based on the simulated results of ANN. The results showed that  coupling of ANN and GIS has a high capability (test stage: Rsr= 0.72) in rangelands cover percent modeling.

Keywords: Rangeland Vegetation, Modeling, MLP, Verification, Mazandaran Province
Full-Text [PDF 422 kb]   (800 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/04/11 | Accepted: 2017/10/29 | Published: 2018/11/24
References
1. Aeinebeygi, S., Khaleghi, M.R., 2016. An Assessment of Biennial Enclosure Effects on Range Production, Condition and Trend (Case Study: Taftazan Rangeland, Shirvan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2): 33-40.
2. Aeinebeygi, S., Khaleghi, M.R., 2016. An Assessment of Biennial Enclosure Effects on Range Production, Condition and Trend (Case Study: Taftazan Rangeland, Shirvan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2): 33-40.
3. Barr T. 2002. Application of tools for hydraulic power point presentation.105-upperGotvand hydroelectric power project feasibility study.1996. Reservior Operation Flood.14p.
4. Dayhoff J. 1990. Natural Networks archictures: Anintroduction. New Yourk: Van NostrandReinhold.
5. Gangopadhyay S., Gautam T., Gupta A. 1999. Subsurface characterization using artificial neural network and GIS Journal of Computing in Civil Engineering, Co13(3):153–161.
6. Gholami V., Chau K.W., Fadaee F., Torkaman J., Ghaffari A. 2015. Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers.Journal of Hydrology,529 (2015) 1060–1069.
7. Hill M. 2001. Flood plain delineation using the HEC-GeoRAS Extention for Arcview. Brigham Young University, 514p.
8. Jafari M., Zare Chahouki M.A., Tavili A., Azarnivand H., Zahedi Amiri GH. 2004. Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran). Journal of Arid Environments,Vol (56): 4, 627-641.
9. Krishna B., Styaji Rao Y.R., vijaya T. 2008. Modeling ground water levels in an urban coastal aquifer using artificial neural networks. Journal of Hydrological process, Vol.22, No.8, PP1180-1188.
10. Krishna M., Neaupane MD., MD Moqbul Hossain. 2008. Predicting Arsenic Concentration in groundwater using GIS-ANN hybrid system, 3rd IASME / WSEAS Int. Conf. on water resources, Hydraulics & Hydrolgeology (WHH '08), University of Cambridge, UK, Feb. 23-25, 2008.21.
11. Nielsen H. R. 1990. Neurocomputing. Addison-Wesley Publ. Co., Reading, MA.
12. McCoord N.M., Illingworth W.T. 1990. A practical guide to neural nets. Addison-Wesley, Publ. Co. the University of Michigan, 344p.
13. Maier H., Dandy G. 2000. Neural networks for the perdictions and forecasting of water resources variables: review of modeling issues and applications. Environmental Modelling and Software, 15,101-124.
14. Melesse A. M., Hanley R. S. 2005. Artificial neural network application for multiecosystem carbon flux simulation. Ecological Modelling, 189, 305-314.
15. Zare Chahouki M.A., KhalasiAhvazi L., Azarnivand H. 2012. Comparison of three modeling approaches for predicting plant species distribution inmountainous scrub vegetation (Semnan ranglands, Iran). Polish Journal of ecology, 60 (2): 277-289.
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Ahmadi Jolandan M, Dianati Tilaki G, Gholami V. Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands. PEC 2018; 6 (12) :153-176
URL: http://pec.gonbad.ac.ir/article-1-309-en.html


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Volume 6, Issue 12 (12-2018) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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