An expert system for predicting the infiltration characteristics
نویسندگان
چکیده
Abstract Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface water quality quantity. This study includes comparative analysis of the two machine learning techniques, M5P model tree (M5P) Gene Expression Programming (GEP), predictions infiltration characteristics. The models were trained tested using 7 combination (CMB1 – CMB7) input parameters; moisture content (m), bulk density soil (D), percentage silt (SI), sand (SA) clay (C), time (t), with output cumulative (CI) rate (IR). Results suggested that GEP has an edge over to predict IR CI R, RMSE MAE values 0.9343, 15.9667 mm/hr & 8.7676 mm/hr, 0.9586, 9.2522 mm 7.7865 for CI, respectively CMB1. Although also gave good results 0.9192, 14.1821 19.2497 0.8987, 11.2144 18.4328 respectively, but lower than GEP. Furthermore, single-factor ANOVA uncertainty used show significance predicted find most efficient soft computing techniques respectively.
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ژورنال
عنوان ژورنال: Water Science & Technology: Water Supply
سال: 2021
ISSN: ['1606-9749', '1607-0798']
DOI: https://doi.org/10.2166/ws.2021.430