PREDICTING SPATIAL VARIABILITY OF SOIL SALINITY AND CLAY CONTENT USING GEOSTATISTICS AND ARTIFICIAL NEURAL NETWORKS METHODS (SHORT TECHNICAL REPORT)

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PAHLAVAN RAD M.R.; DEHGHANI A.A.; SHAHRIARI A.; AKBARI MOGHADDAM A.R.; DAHMARDEH KH.

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Journal: Journal of Soil Management and Sustainable Production Year:2016 | Volume:6 | Issue:1 Page(s): 247-254

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Title

PREDICTING SPATIAL VARIABILITY OF SOIL SALINITY AND CLAY CONTENT USING GEOSTATISTICS AND ARTIFICIAL NEURAL NETWORKS METHODS (SHORT TECHNICAL REPORT)

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Keywords

NEURAL NETWORK METHODQ1

GEOSTATISTICAL METHODQ1

ECQ2

ZAHAK CITYQ1

SISTAN PLAINQ1

Abstract

Background and Objectives: Soil maps are the major sources for lands management, natural resources and environmental aspects. Soil salinity is one of the most important factor of limiting product and soil clay also has important effects on soil fertility in ZAHAK CITY in SISTAN PLAIN.Accurate maps of soil salinity status and clay content could highly help to manage the proper of lands in this area. There are different techniques for prediction of soil characteristics in not sampled areas such as geostatistics and artificial neural networks (ANN) which were used in this study.Materials and Methods: This investigation were done to compare geostatistical and artificial neural networks (ANN) methods in predicting soil salinity and clay content in ZAHAK CITY in SISTAN PLAIN.121 soil samples were taken from the depth of 0-30 cm within the grid of 750×750 m and soil EC and clay percentage were determined.105 samples were used for training and 16 samples were used for validation in both models. The different models were fitted for each technique and the best models were selected and independent validation was used to determine accuracy of two techniques.Results: Results showed that soil was affected by salinity in study area. ANN with R2=0.59 and RMSE=10.2 comparing to geostatistics with R2=0.53 and RMSE=12.3 had more accuracy in soil salinity prediction. Also in predicting soil clay content, ANN estimated better compared to geostatistics with R2=0.67 and RMSE=6.18 against R2=0.62 and RMSE=8.2.Conclusion: The accuracy of NEURAL NETWORK METHOD to estimate the soil salinity and clay content was slightly higher than GEOSTATISTICAL METHOD. In both techniques the accuracy of estimation for soil salinity was lower than clay content that can be related to management effect on soil salinity in study area. Irrigation and agricultural activities caused leaching salts and reducing soil salinity.

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APA: Copy

PAHLAVAN RAD, M.R., DEHGHANI, A.A., SHAHRIARI, A., AKBARI MOGHADDAM, A.R., & DAHMARDEH, KH.. (2016). PREDICTING SPATIAL VARIABILITY OF SOIL SALINITY AND CLAY CONTENT USING GEOSTATISTICS AND ARTIFICIAL NEURAL NETWORKS METHODS (SHORT TECHNICAL REPORT). ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, 6(1), 247-254. SID. https://sid.ir/paper/209862/en

Vancouver: Copy

PAHLAVAN RAD M.R., DEHGHANI A.A., SHAHRIARI A., AKBARI MOGHADDAM A.R., DAHMARDEH KH.. PREDICTING SPATIAL VARIABILITY OF SOIL SALINITY AND CLAY CONTENT USING GEOSTATISTICS AND ARTIFICIAL NEURAL NETWORKS METHODS (SHORT TECHNICAL REPORT). ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION[Internet]. 2016;6(1):247-254. Available from: https://sid.ir/paper/209862/en

IEEE: Copy

M.R. PAHLAVAN RAD, A.A. DEHGHANI, A. SHAHRIARI, A.R. AKBARI MOGHADDAM, and KH. DAHMARDEH, “PREDICTING SPATIAL VARIABILITY OF SOIL SALINITY AND CLAY CONTENT USING GEOSTATISTICS AND ARTIFICIAL NEURAL NETWORKS METHODS (SHORT TECHNICAL REPORT),” ELECTRONIC JOURNAL OF SOIL MANAGEMENT AND SUSTAINABLE PRODUCTION, vol. 6, no. 1, pp. 247–254, 2016, [Online]. Available: https://sid.ir/paper/209862/en

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