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

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>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ali Akbar Moosavi (17769033) (author)
مؤلفون آخرون:	Mohammad Amin Nematollahi (17769036) (author), Mohammad Omidifard (20209150) (author)
منشور في:	2024
الموضوعات:	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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