Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models

<table>Measured versus predicted <svg height="15.4" id="M99" style="vertical-align:-3.3907pt" version="1.1" viewbox="0 0 18.4 15.4" width="18.4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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q12 68 2 83t-72 22l6 28h257l-7 -28q-62 -7 -76 -21.5t-27 -83.5l-32 -172q29 11 78 46q112 84 217 179q18 16 23.5 25.5t-1 14.5t-26.5 8l-30 4l5 28h249z" id="x1D43E"></path></g>
<g transform="matrix(.012,-0,0,-.012,13.25,15.187)"><path d="M352 391q0 -31 -27 -44q-14 -7 -24 6q-39 48 -84 48q-23 0 -39.5 -15t-16.5 -40q0 -43 73 -90q49 -32 70 -58t21 -57q0 -58 -62 -105.5t-129 -47.5q-40 0 -75.5 25t-35.5 52q0 28 32 46q7 4 15 3t11 -6q19 -31 48.5 -50.5t54.5 -19.5q34 0 54 19.5t20 42.5q0 43 -65 81
q-97 56 -97 123q0 50 51 96q19 17 58 32.5t62 15.5q37 0 61 -18t24 -39z" id="x1D460"></path></g>
</svg> values using MLR.</table>

International Scholarly Research Notices

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Figure 3

Figure 3: Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models 