Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province

Abstract

Article Info Received : 01.08.2013 Accepted : 29.10.2013 The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R 2 , RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data.

Estimation of infiltration rate from soil properties using regression model for cultivated land

“…Soil properties are one of the important parameter which governs the rate of infiltration. Design, operation, management, and hydraulic evaluation of on-farm water applications have also rely on the infiltration properties of the soil because infiltration behavior of the soil directly determines the essential variables such as inflow rate, length of run, application time, depth of percolation, and tailwater run-off in irrigation systems (Adeniji et al, 2013;Sarmadian and Taaghizadeh-Mehrjardi 2014). Martens and Frankenberger (1992) have carried out the work on the modification of infiltration rates in an organicamended irrigated soil.…”