Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network

Introduction Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode...

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Autores principales:	J Baradaran Motie, M. H Aghkhani, A Rohani, A Lakziyan
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2019
Materias:	rbf neural networks soil salinity apparent electrical conductivity actual electrical conductivity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/a8ce7c7dbe754a28b8f5d0c05a2e4eda
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id	oai:doaj.org-article:a8ce7c7dbe754a28b8f5d0c05a2e4eda
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN FA
topic	rbf neural networks soil salinity apparent electrical conductivity actual electrical conductivity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	rbf neural networks soil salinity apparent electrical conductivity actual electrical conductivity Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 J Baradaran Motie M. H Aghkhani A Rohani A Lakziyan Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
description	Introduction Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode contact method (Wenner method) is one of the widely used methods to rapidly measure soil ECa in farms. However, soil scientists prefer soil actual electrical conductivity (saturated extract electrical conductivity) (ECe) as an indicator of soil salinity, though its measurement is only possible in the laboratory. The aim of this study was to find a relationship between the prediction of soil actual electrical conductivity (ECe) in terms of temperature, moisture, bulk density and apparent electrical conductivity of soil (ECa). Thereby, the estimation of ECe would allow the partial calculation of ECa that is dependent upon soil salinity and dissolved salts. Materials and Methods This study used RBF neural network in Box-Behnken statistical design to explore the impacts of effective parameters on direct contact method in the measurement of soil ECa and provided a model to estimate ECe from ECa, temperature, moisture content and bulk density. In this study soil apparent electrical conductivity (ECa) was measured by direct contact (Wenner) method. The present study considered four most effective factors: ECa (saturated paste extract EC), moisture, bulk density, and temperature (Baradaran Motie et al., 2010). Given the characteristics of farming soils in Khorasan Razavi Province (Iran), the maximum and minimum of each independent variable were assumed as 0.5-6 mS.cm-1 for ECe, 5-25% for moisture content, 1-1.8 g.cm-3 for bulk density, and 2-37°C for soil temperature. Considering the experimental design, three moisture levels (5, 15 and 25%), three salinity levels (0.5, 3.25 and 6 mS.cm-1), three temperature levels (2, 19 and 37°C) and three compaction levels with bulk densities of 1, 1.4 and 1.8 g.cm-3 were assumed in 27 trials with predetermined arrangement on the basis of Box-Behnken technique. 13 common algorithms were explored in MATLAB software package for the training of the artificial neural network in order to find the optimum algorithm (Table 4). The input layer of the network designed by integrating a Randomized Complete Block Design (RCBD) with k-fold cross-validation. Using k-fold cross-validation, 20 different datasets were generated for training and validation of RBF neural network. Results and Discussion A combination of an RCBD and k-fold cross-validation was used. The results of both training and validation phases should be considered in the selection of training algorithm. In addition, R2 of T1 training algorithm had a much lower standard deviation than other training algorithms. The lower standard deviation is, the more capable the algorithm would be in learning from different datasets. Considering all aspects, trainbr (T2) training algorithm was found to have the best performance among all 13 training algorithms of the neural network. Table 7 tabulates the results of means comparison for R2 of RBF model for both training and validation phases resulted from the application of some combinations of S and L2 factors as interaction. As can be observed, R2 = 0.99 for all of them with no significant difference. However, the magnitude of order differed between training and validation phases. Given the importance of the training phase, L2=9 and S=0.1 were regarded as the optimum values. The sensitivity analysis of the network revealed that soil ECa, moisture, bulk density, and temperature had the highest to lowest impact on the estimation of soil ECe, respectively. This model can improve the precision of soil ECa measurement systems in the estimation and preparation of soil salinity maps. Furthermore, this model can save in time of data analysing and soil EC mapping because it does not need data recollection for the calibration of systems. A validation prose was done with a 60 field collected data set. The results of validation show R2=0.986 between predicted and measured ECa. Conclusions The present research focused on improving the precision of soil ECe measurement on the basis of easily accessible parameters (ECa, temperature, moisture, and bulk density). In conventional methods of soil EC mapping, the systems only measure soil ECa and then calibrate it to ECe by collecting some samples and using statistical methods. In this study, Soil ECe was estimated with R2 = 0.99 by a multivariate artificial neural network model with the inputs, including ECa, temperature, moisture, and bulk density of soil without any need to collect further soil samples and calibration process. The Bayesian training algorithm was introduced as the best training algorithm for this neural network. Thereby, soil EC variation maps can be prepared with higher precision to estimate the spatial spread of salinity in farms. Also, the results imply that soil ECa, moisture, bulk density and temperature have the highest to lowest effectiveness on the estimation of soil ECe, respectively.
format	article
author	J Baradaran Motie M. H Aghkhani A Rohani A Lakziyan
author_facet	J Baradaran Motie M. H Aghkhani A Rohani A Lakziyan
author_sort	J Baradaran Motie
title	Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
title_short	Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
title_full	Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
title_fullStr	Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
title_full_unstemmed	Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network
title_sort	modeling the effective parameters on accuracy of soil electrical conductivity measurement systems using rbf neural network
publisher	Ferdowsi University of Mashhad
publishDate	2019
url	https://doaj.org/article/a8ce7c7dbe754a28b8f5d0c05a2e4eda
work_keys_str_mv	AT jbaradaranmotie modelingtheeffectiveparametersonaccuracyofsoilelectricalconductivitymeasurementsystemsusingrbfneuralnetwork AT mhaghkhani modelingtheeffectiveparametersonaccuracyofsoilelectricalconductivitymeasurementsystemsusingrbfneuralnetwork AT arohani modelingtheeffectiveparametersonaccuracyofsoilelectricalconductivitymeasurementsystemsusingrbfneuralnetwork AT alakziyan modelingtheeffectiveparametersonaccuracyofsoilelectricalconductivitymeasurementsystemsusingrbfneuralnetwork
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spelling	oai:doaj.org-article:a8ce7c7dbe754a28b8f5d0c05a2e4eda2021-11-14T06:34:54ZModeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network2228-68292423-394310.22067/jam.v9i1.66489https://doaj.org/article/a8ce7c7dbe754a28b8f5d0c05a2e4eda2019-03-01T00:00:00Zhttps://jame.um.ac.ir/article_33528_1c07b42fe6fe890cd7d6c68bfc331ccb.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Introduction Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode contact method (Wenner method) is one of the widely used methods to rapidly measure soil ECa in farms. However, soil scientists prefer soil actual electrical conductivity (saturated extract electrical conductivity) (ECe) as an indicator of soil salinity, though its measurement is only possible in the laboratory. The aim of this study was to find a relationship between the prediction of soil actual electrical conductivity (ECe) in terms of temperature, moisture, bulk density and apparent electrical conductivity of soil (ECa). Thereby, the estimation of ECe would allow the partial calculation of ECa that is dependent upon soil salinity and dissolved salts. Materials and Methods This study used RBF neural network in Box-Behnken statistical design to explore the impacts of effective parameters on direct contact method in the measurement of soil ECa and provided a model to estimate ECe from ECa, temperature, moisture content and bulk density. In this study soil apparent electrical conductivity (ECa) was measured by direct contact (Wenner) method. The present study considered four most effective factors: ECa (saturated paste extract EC), moisture, bulk density, and temperature (Baradaran Motie et al., 2010). Given the characteristics of farming soils in Khorasan Razavi Province (Iran), the maximum and minimum of each independent variable were assumed as 0.5-6 mS.cm-1 for ECe, 5-25% for moisture content, 1-1.8 g.cm-3 for bulk density, and 2-37°C for soil temperature. Considering the experimental design, three moisture levels (5, 15 and 25%), three salinity levels (0.5, 3.25 and 6 mS.cm-1), three temperature levels (2, 19 and 37°C) and three compaction levels with bulk densities of 1, 1.4 and 1.8 g.cm-3 were assumed in 27 trials with predetermined arrangement on the basis of Box-Behnken technique. 13 common algorithms were explored in MATLAB software package for the training of the artificial neural network in order to find the optimum algorithm (Table 4). The input layer of the network designed by integrating a Randomized Complete Block Design (RCBD) with k-fold cross-validation. Using k-fold cross-validation, 20 different datasets were generated for training and validation of RBF neural network. Results and Discussion A combination of an RCBD and k-fold cross-validation was used. The results of both training and validation phases should be considered in the selection of training algorithm. In addition, R2 of T1 training algorithm had a much lower standard deviation than other training algorithms. The lower standard deviation is, the more capable the algorithm would be in learning from different datasets. Considering all aspects, trainbr (T2) training algorithm was found to have the best performance among all 13 training algorithms of the neural network. Table 7 tabulates the results of means comparison for R2 of RBF model for both training and validation phases resulted from the application of some combinations of S and L2 factors as interaction. As can be observed, R2 = 0.99 for all of them with no significant difference. However, the magnitude of order differed between training and validation phases. Given the importance of the training phase, L2=9 and S=0.1 were regarded as the optimum values. The sensitivity analysis of the network revealed that soil ECa, moisture, bulk density, and temperature had the highest to lowest impact on the estimation of soil ECe, respectively. This model can improve the precision of soil ECa measurement systems in the estimation and preparation of soil salinity maps. Furthermore, this model can save in time of data analysing and soil EC mapping because it does not need data recollection for the calibration of systems. A validation prose was done with a 60 field collected data set. The results of validation show R2=0.986 between predicted and measured ECa. Conclusions The present research focused on improving the precision of soil ECe measurement on the basis of easily accessible parameters (ECa, temperature, moisture, and bulk density). In conventional methods of soil EC mapping, the systems only measure soil ECa and then calibrate it to ECe by collecting some samples and using statistical methods. In this study, Soil ECe was estimated with R2 = 0.99 by a multivariate artificial neural network model with the inputs, including ECa, temperature, moisture, and bulk density of soil without any need to collect further soil samples and calibration process. The Bayesian training algorithm was introduced as the best training algorithm for this neural network. Thereby, soil EC variation maps can be prepared with higher precision to estimate the spatial spread of salinity in farms. Also, the results imply that soil ECa, moisture, bulk density and temperature have the highest to lowest effectiveness on the estimation of soil ECe, respectively.J Baradaran MotieM. H AghkhaniA RohaniA LakziyanFerdowsi University of Mashhadarticlerbf neural networkssoil salinityapparent electrical conductivityactual electrical conductivityAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 9, Iss 1, Pp 139-154 (2019)

Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network

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