Spatio-temporal prediction of soil salinity using soft data and Bayesian maximum entropy method in western shores of Urmia Lake

Document Type : Original Article

Abstract

Real-time monitoring of soil salinity changes is quite costly, so that sound methods which could improve the quality of the predictions would thus be a step towards an improved and sustainable salinity hazards monitoring system on the long run. The aim of this paper is to propose such a methodological framework, with an illustration based on the implication of the calculated error caused by field measurements in one time interval to improve spatiotemporal predictions of soil salinity where no laboratory measurements are available. Soil EC was measured in the field for all times series but only for the first, second and fifth data sets laboratory measurements were implemented. After calibrating field EC by laboratory measurements for the first two datasets, histograms of residuals were calculated and then variance of the residuals were taken into account as error and were used in soil salinity prediction using Bayesian Maximum Entropy method (BME) in other time series. Results from validation of the predicted values for soil salinity revealed that implementation of calibration line and the calculated error for one time interval in BME equations could successfully improve soil salinity prediction during other time intervals with validation results of ME and MSE equal to -0.12 and 0.72 for 5th dataset. Therefore, calibration line based on first two datasets was applied in spatiotemporal prediction of soil salinity in all-time series. Results showed that soil salinity has increased during time interval of 2010-2016 and secondary salinization has been occurring in agricultural lands. Mean soil salinity has increased from 4.56 dS/m in 2011 to 6.65 in 2016. The reduced need for constant calibration of field measured data and number of soil samples using soft data and BME method will make soil salinity monitoring possible where there is a great need for careful monitoring.

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References

References
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  • Receive Date: 20 November 2017
  • Revise Date: 19 December 2018
  • Accept Date: 02 November 2017

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