Comparing nonparametric k-nearest neighbor technique with ANN model for predicting soil saturated hydraulic conductivity
Author(s):
Abstract:
Soil saturated hydraulic conductivity is the most important physical parameter, but its measurement often is difficult because of practical and/or cost-related reasons. In this research, expert system approaches with one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm, was compared and tested to estimate saturated hydraulic conductivity (Ks) from other easily available soil properties. In this research 151 soil samples were collected from farms land around Bojnourd and saturated hydraulic conductivity (Ks) was estimated from other soil properties including soil textural fractions, EC, pH, SP, OC, TNV, ρs and ρb. Results showed that the accuracy of the parameter estimation, using parametric method of artificial neural network to compare with k-nearest neighbors for terms of all the parameters (with r=0.97, EF=0.946, RMSE= 8.798, ME= 28.446 and CRM =-0.144) compared to other methods input models is acceptable and can used to estimate saturated hydraulic conductivity especially when for new data set available these functions is essential.
Keywords:
Language:
Persian
Published:
Soil Management and Sustainable Production, Volume:5 Issue: 3, 2016
Pages:
81 to 95
magiran.com/p1517738
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یکساله به مبلغ 1,390,000ريال میتوانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
توجه!
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.
In order to view content subscription is required
Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!