TITLE:
Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)
AUTHORS:
Ali Haghizadeh, Leila Soleimani, Hossein Zeinivand
KEYWORDS:
Infiltration, Green-Ampt Empirical Model, WMS Model, Artificial Neural Network Model (ANN)
JOURNAL NAME:
Journal of Water Resource and Protection,
Vol.6 No.5,
April
25,
2014
ABSTRACT: Determination
of the infiltration rate in a watershed is not easy and in empirical and
theoretical point of view, it is important to access average value of
infiltration. Infiltration models has main role in managing water sources.
Therefore different types of models with various degrees of complexity were
developed to reach this aim. Most of the estimating methods of soil
infiltration are expensive and time consuming and these methods estimate
infiltration with hypothesis of zero slope. One of the conceptual and physical
models for estimating soil infiltration is Green-Ampt model which is similar to
Richard model. This model uses slope factor in estimating infiltration and this
is the power point of Green-Ampt model. In this research the empirical model of
Green-Ampt was optimized with integrating artificial neural network model (ANN)
and a model of geographical information system WMS to estimate the infiltration
in Kakasharaf watershed. Results of the comparison between the output of this
method and real value of infiltration in region (through multiple cylinders)
showed that this method can estimate the infiltration rate of Kakasharaf
watershed with low error and acceptable accuracy (Nash-Sutcliff performance
coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model
error 0.024).