Statistical indices between the measured field-saturated hydraulic conductivity () and RBFNNs-predicted field-saturated hydraulic conductivity () of different data sets using different input groups.

<p>Statistical indices between the measured field-saturated hydraulic conductivity () and RBFNNs-predicted field-saturated hydraulic conductivity () of different data sets using different input groups.</p>

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Bibliographic Details
Main Author:	Ali Akbar Moosavi (17769033) (author)
Other Authors:	Mohammad Amin Nematollahi (17769036) (author), Mohammad Omidifard (20209150) (author)
Published:	2024
Subjects:	Neuroscience Biotechnology Cancer Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified traditional regression model test data set particle swarm optimization hydrological modeling frameworks highest correlation coefficient geometric mean diameter calcium carbonate equivalent using statistical indices statistical assessment indicated saturated hydraulic conductivity radial basis functions 83 ), mlpnns 13 ), rbfnns prediction models ranked div >< p artificial neural networks 47 ), ga neural networks statistical predictors pedotransfer functions electrical conductivity mwd ), ec ), 30 ), xlink "> wider potential value provided usually integrated soil environment soil conditions robust prediction results revealed potential uncertainties input variables including variants geographical locations existing measurements evaluations may crucial components chemical inputs bulk density associated laboratory
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Statistical indices between the measured field-saturated hydraulic conductivity () and RBFNNs-predicted field-saturated hydraulic conductivity () of different data sets using different input groups.

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