SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS¹

Abstract

This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.

Compilation of a national soil-type map for Hungary by sequential classification methods

“…ANNs are current methods in DSM, e.g. predicting soil physical properties (Chang and Islam, 2000;Pachepsky et al, 1996), soil chemical properties (Amini et al, 2005;Patel et al, 2002;Saffari et al, 2009), yield prediction (Dai et al, 2011;Kaul et al, 2005), and soil erosion (Kim and Gilley, 2008). Behrens et al (2005) mapped soil units in a German sample area and found the predictive power of ANNs considerably high.…”

Identifying and managing risk factors for salt-affected soils: a case study in a semi-arid region in China

“…Statistical and neural networks models have been used for soil salinity risk identification and for further exploration of the relationship between soil salinization and its risk factors (Bradd et al 1997;Patel et al 2002;Triantafilis et al 2004;Wang et al 2008;Akramkhanov and Vlek 2012). However, traditional statistical techniques such as linear regression model (Zhang et al 2010b), multiple gray relation model (Rao and Yadava 2009), and system dynamic model (Ali Kerem and Yaman 2001) are limited when they are used to analyze spatial data due to spatial autocorrelations in geographic variables (Overmars et al 2003;Merckx et al 2011;Naimi et al 2011).…”

The assessment of spatial distribution of soil salinity risk using neural network

“…It is able to modify its structure by learning from the data rather than being programmed, and is thus able to solve complicated relations (Principe et al 2000). In salinity studies, ANN have been used for a variety of purposes, including the prediction of a salt build-up in the crop root zone (Patel et al 2002), dielectric constant-soil water relationships (Persson et al 2002), and river water salinity forecasting (Bowden et al 2002;Maier and Dandy 1999).…”