کالیبراسیون عمومی TDR برای ارزیابی رطوبت خاک های مناطق گرمسیری با استفاده از شبکه های عصبی مصنوعی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|52566||2015||10 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Hydrology, Volume 530, November 2015, Pages 657–666
Determinations of soil moisture are important for various agricultural, environmental and hydrological applications, and accurate assessments are required. Artificial neural networks (ANNs) were applied in the present study to conduct general calibrations of time-domain reflectometry (TDR) probes using the physical characteristics of soil to estimate the moisture. The ANNs were trained and tested using data from five different soils. All of the combinations of physical properties, including the bulk density and sand, silt, clay and organic matter contents, were tested, and the inclusion of at least one of those network variables along with the apparent dielectric constant (Ka), which was assessed using the TDR device, were sufficient to calibrate all five of the soils simultaneously. The ANN selected for the general calibration has a hidden layer with 13 neurons and tan-sigmoid-type transfer function. The analysis of the statistical indexes values indicates that the ANNs were slightly better than the third-order polynomial equations (Topp-like equations), which were specifically fitted to each soil. The tests were conducted to assess the performance of the general calibrations that were applied to estimate the moisture of the soils excluded from the training process, although the ANNs have such a potential; the most representative variables in descending order of importance were as follows: organic matter, sand, clay, and bulk density. The soil silt content failed to stand out in this analysis and showed a lower performance. Based on the results, the organic matter content was the preferred variable for use along with the Ka in the ANNs applied to the general calibration of TDR (RMSE ranging from 0.0126 to 0.0237 g/g and r2 ranging from 0.9083 to 0.9891). The sand content was also considered an advantageous variable because it was more easily assessed. The variables clay content and bulk density or the combination of several variables may also be used when available. As found by previous studies, the TDR calibration using ANN were better to sandy soils.