Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

<div>Monthly precipitation (<svg height="8.64656pt" id="M5" style="vertical-align:-0.04981995pt" version="1.1" viewbox="-0.0498162 -8.59674 8.21594 8.64656" width="8.21594pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M622 479C622 589 545 650 400 650H146L140 618C229 609 230 602 215 527L135 132C117 43 112 41 27 32L18 0H305L313 32C213 40 205 44 222 132L251 284L324 262H365C484 269 622 333 622 479ZM528 477C528 356 420 305 344 305C300 305 276 314 259 321L303 554C311 597 313 600 331 606C345 611 361 614 379 614C463 614 528 580 528 477Z" id="g50-81"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="640" vert-adv-y="640"></glyph.data></g></svg>) and evapotranspiration (ETo) versus time in period 1990–2010.</div>

The Scientific World Journal

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Figure 2

Figure 2: Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS 