دانلود مقاله ISI انگلیسی شماره 52507
عنوان فارسی مقاله

نقشه برداری خاک دیجیتال با استفاده از شبکه های عصبی مصنوعی و ویژگی زمین های مرتبط

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52507 2015 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Pedosphere, Volume 25, Issue 4, August 2015, Pages 580–591

کلمات کلیدی
ویژگی های مدل ارتفاعی رقومی - پرسپترون چند لایه - طبقه بندی خاک - خاک - تشکیل عوامل - بررسی خاک
پیش نمایش مقاله
پیش نمایش مقاله نقشه برداری خاک دیجیتال با استفاده از شبکه های عصبی مصنوعی و ویژگی زمین های مرتبط

چکیده انگلیسی

Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.