|تعداد مشاهده مقاله||7,209,755|
|تعداد دریافت فایل اصل مقاله||5,601,460|
Rangelands production modeling using an artificial neural network (ANN) and geographic information system (GIS) in Baladeh rangelands, North Iran
|Caspian Journal of Environmental Sciences|
|دوره 18، شماره 3، مهر 2020، صفحه 277-290 اصل مقاله (1.17 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22124/cjes.2020.4139|
|Ghasem Ali Dianati Tilaki1؛ Maryam Ahmadi Jolandan1؛ Vahid Gholami 2|
|1Department of Range Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran|
|2Department of Range and Watershed Management and Dept. of Water Eng. and Environment, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran|
|Rangelands production measurement is time-consuming and expensive. Therefore, models are often employed to simulate rangelands conditions as a supplement. Artificial neural network (ANN) is widely used for modeling in environmental studies, yet it cannot preset its results in the form of a map or geo-referenced data. We used ANN to estimate the spatial distribution of rangelands production, then a geographic information system (GIS) was applied as a pre-processing and post-processing framework in rangelands production modeling. The ANN was trained (Rsqr = 0.95, MSE = 0.02) and tested using data from the Baladeh rangelands located in the northern part of Iran. Rangelands production was simulated using a multi-layer perceptron (MLP) network. We estimated rangelands production (using many plots and field studies) as the network output, along with the influencing factors in the production (vegetation, climatic, topographic, edaphic and human factors) as the inputs. After modeling and model optimizing in ANN, the model test was performed (Rsqr=0.8, MSE=0.3). Furthermore, the studied area was divided with the pixels 100×100 m (raster format) in the GIS medium. Then, the digital layers of the network inputs were combined and a raster layer was prepared including the network inputs values and geographic coordinate. The values of pixels (network inputs) were imported in ANN (NeuroSolutions software). Rangelands production was simulated using the validated optimum network in the sites without production measurements. In the next step, the results of ANN simulation were imported in the GIS medium, then rangelands production map was prepared based on the estimated results of ANN. The results indicated that integrating ANN and GIS exhibits high accuracy and performance in rangelands production estimation. Hence, the prepared rangelands production map can be used for planning and managing the rangelands.|
|Production measurement؛ MLP network؛ Rangelands production map؛ Iran|
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