کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4681308 1635093 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A neural network model for estimating soil phosphorus using terrain analysis
ترجمه فارسی عنوان
مدل شبکه عصبی برای برآورد فسفر خاک با استفاده از تحلیل زمین
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
چکیده انگلیسی

Artificial neural network (ANN) model was developed and tested for estimating soil phosphorus (P) in Kouhin watershed area (1000 ha), Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern across the topographically heterogeneous landscape, the topographic and vegetation attributes were used in addition to pedologic information for the development of ANN model in area for estimating of soil phosphorus. Totally, 85 samples were collected and tested for phosphorus contents and corresponding attributes were estimated by the digital elevation model (DEM). In order to develop the pedo-transfer functions, data linearity was checked, correlated and 80% was used for modeling and ANN was tested using 20% of collected data. Results indicate that 68% of the variation in soil phosphorus could be explained by elevation and Band 1 data and significant correlation was observed between input variables and phosphorus contents. There was a significant correlation between soil P and terrain attributes which can be used to derive the pedo-transfer function for soil P estimation to manage nutrient deficiency. Results showed that P values can be calculated more accurately with the ANN-based pedo-transfer function with the input topographic variables along with the Band 1.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: The Egyptian Journal of Remote Sensing and Space Science - Volume 18, Issue 2, December 2015, Pages 127–135
نویسندگان
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