本研究主要目的將現地資料含水量，運用模糊類神經網路去做訓練，在考慮降雨與土壤含水量，進行未飽和層剖面之地表入滲、逕流分析與推估。
選定本校資源系系館後方作為試驗地點，收集試驗地點之含水量資料，進行入滲量及逕流量之推估。此地點之土壤剖面則依照不同深度進行含水量的觀測，經由模糊類神經網路訓練後，進行土壤入滲行為之分析，進而推求此區域的地表下入滲量與逕流量。
根據結果顯示，深度0至15公分入滲量為347公釐、逕流量為149公釐；深度15至30公分入滲量為253公釐、逕流量為94公釐；深度30至45公分入滲量為143公釐、逕流量為110公釐。降雨量與含水量的平均相關係數則為0.95。

The propose of net fuzzy-neuro is to train water contents. This dissertation considers rainfall and water contents to estimate the infiltration and runoff in unsaturated soil profiles.
In addition , at NCKU-RE study site , collections of soil water contents and data of rainfall estimate infiltration and runoff in homogeneous unsaturated soil profiles ,after net fuzzy-neuro training ,to analyze soil infiltration behavior and determine the infiltration and runoff of the region of the subsurface.
The results of analysis showed that infiltration 347mm and runoff 149mm at subsurface 0~15 cm , infiltration 253mm and runoff 94mm at subsurface 15~30 cm , infiltration 143mm and runoff 110mm at subsurface 30~45 cm . Between the rainfall and water content correlation factor is 0.95.

Keyword: unsaturated zone , fuzzy-neuro , infiltration and runoff.

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