Performance Evaluation of MLP and RBF Neural Networks to Estimate the Soil Saturated Hydraulic Conductivity
نویسندگان
چکیده
منابع مشابه
using artificial neural networks to estimate saturated hydraulic conductivity from easily available soil properties
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متن کاملAn evaluation of genetic algorithm method compared to geostatistical and neural network methods to estimate saturated soil hydraulic conductivity using soil texture
ABSTRACT-Determining hydraulic conductivity of soil is difficult, expensive, and time-consuming. In this study, Algorithm Genetic and geostatistical analysis and Neural Networks method are used to estimate soil saturated hydraulic conductivity using the properties of particle size distribution. The data were gathered from 134soil profiles from soil and lander form studies of the Ardabil Agricul...
متن کاملapplication of artificial neural networks in prediction of saturated hydraulic conductivity using soil physical parameters
soil hydraulic properties such as saturated and unsaturated hydraulic conductivity play an important role in environmental research. since direct measurement of these soil hydraulic properties is time-consuming and costly, indirect methods such as pedotransfer functions and artificial neural networks (ann) were developed based on readily available parameters. in this study, the use of ann to pr...
متن کاملChanges of the Saturated Hydraulic Conductivity of Soil Influenced and Exchangeable Sodium Percentage (%ESP)
Sodic soil is one of the most common problems of limiting agricultural production that related on irrigation. Saturated hydraulic conductivity is one of hydrodynamic characteristics in evaluating transmission flow through underground levels. To investigate the role of increment of exchangeable sodium in hydraulic conductivity changes (as characterized by the mobility of water in soil) an experi...
متن کاملمقایسه روش‌های شبکه عصبی مصنوعی و رگرسیونی برای پیش‌بینی هدایت هیدرولیکی اشباع خاک‌های استان خوزستان
Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are t...
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ژورنال
عنوان ژورنال: Modern Applied Science
سال: 2016
ISSN: 1913-1852,1913-1844
DOI: 10.5539/mas.v11n3p1