Dynamic prediction of soil salinization in an irrigation district based on the support vector machine

Abstract Soil salinization has random characteristics because of the influences of natural and anthropogenic factors. Therefore, a study on the dynamic prediction model of soil salinity is important for irrigation water management in salinization irrigation districts. In the present paper, the theory of supporting vector machine was introduced in the dynamic prediction of soil electrical conductivity (EC) values. Based on groundwater depth, irrigation water volume, and evaporation data from 1991 to 2010 in the Shahaoqu subdistrict of the Hetao irrigation district in Inner Mongolia, a dynamic prediction model of soil EC value was developed. The results show that the fitted values, tested values and the predicted values of the model have little difference from the real values. The absolute value of the fitting mean relative error is 2.14%, that of the testing mean relative error is 3.48%, and that of the predicting mean relative error is 6.37%. Compared with the Artificial Neural Network (ANN) model, the results show that SVM predictors perform better in forecasting soil EC values than the ANN model. The feasibility of the application of SVMs to soil salinity forecasting was demonstrated and their performance in soil salinity data analysis was proven.

[1]  Mac McKee,et al.  Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter 1 , 2007 .

[2]  Ding Jian-li.  Soil Salinization Monitoring Based on Support Vector Machine Classification , 2007 .

[3]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[4]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[5]  J. D. Rhoades,et al.  Soil salinity assessment :methods and interpretation of electrical conductivity measurements , 1999 .

[6]  Liu Hong,et al.  Study of Soil Water Forecast with Neural Network , 2003 .

[7]  T. Darwish,et al.  Salinity evolution and crop response to secondary soil salinity in two agro-climatic zones in Lebanon , 2005 .

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Li Qinghua.  Statistical Learning Theory and Support Vector Machine , 2005 .

[10]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[11]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[12]  Zhongbo Yu,et al.  Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction , 2010 .

[13]  P. Crosson,et al.  World agriculture: Toward 2000, an FAO study , 1996 .

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[16]  Tian Yuan,et al.  Extracting of soil salinization by SVM and accuracy evaluation based on texture characteristic. , 2009 .

[17]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[18]  R. Reich,et al.  Soil Salinity Sampling Strategy Using Spatial Modeling Techniques, Remote Sensing, and Field Data , 2008 .

[19]  Abdelhamid A. Elnaggar,et al.  Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas , 2009, Remote. Sens..

[20]  Liming Liu,et al.  A method of salt-affected soil information extraction based on a support vector machine with texture features , 2010, Math. Comput. Model..

[21]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .