Investigating the Performance of Data-based Methods in Estimating Important Moisture Points in Shahrood Area

Document Type : Research Paper


1 MSc of Irrigation Drainage, Zabol University, Iran

2 MSc of Watershed Management, Zabol University, Iran.

3 MSc of Soil Engineering, Shahrood University, Iran.


Awareness of the important moisture points is crucial for irrigation studies on the farm, but measuring this information in a direct way is very costly and time consuming. Therefore, several models and relationships have been developed as Pedotransfer functions which indirectly predict the hydrological properties of the soil using readily available soil data with the aid of a series of proper mathematical relationships (Nguyen et al., 2015). Since the measurement of important moisture points is a time consuming, costly and difficult work, many attempts have been made in order to use simpler soil properties such as texture, the amount of organic matter, and bulk density. Pedotransfer functions are indeed predictive functions which establish relationship between the soil’s readily available and latency data (e.g., the percentage of sand, silt and clay, bulk density and organic matter) including the parameters of the moisture curve (field capacity and permanent wilting point) (Botulla et al., 2013). Moreover, the functions that can be successfully implemented in an area may not have suitable adaptations in another area with real values. There are several methods for obtaining Pedotransfer functions, among them  are linear regression (LR), artificial neural networks, fuzzy adaptive-neural inference, and support regression vector.
 Various researchers have studied the development of Pedotransfer functions and evaluated the predictive models in the water and soil sciences. As a sample, Shop and Lajj (1998) estimated the soil moisture curve using the neural network. They found that the artificial neural network was better than some of the regression Pedotransfer functions provided by other researchers, and if more readily available properties were used as inputs, the prediction accuracy increased. However, there was always a significant difference between the predicted and measured moisture values. Zhang et al. (2007) estimated the soil moisture curve for 110  non-calcareous soil samples with different tissue classes through the artificial neural networks and regression models. They showed that the neural network predicts the moisture curve better than the regression method with higher correlation coefficient in most tissue classes. Lin et al. (2009) argued that the SVM method was much faster trained than the artificial neural network. SVM was also found to have a more accurate prediction than the artificial neural network. Chen et al. (2010) used support vector machines to model daily rainfall and compared the results with that of the multivariate analysis method. It was found that the results of predictions from SVM were more accurate. In turn, Kaihua et al. (2014) used support vector machines to predict cationic capacity on different horizons of the soil in Qingdao, China. They performed their studies at 208 points on two horizons of the soil, and concluded that the SVM model improved predictions.
Considering the significance of knowing the important points of soil moisture in Shahrood area for agricultural projects and irrigation schedules, developing appropriate Pedotransfer functions and evaluating models is necessary so as to obtain moisture of the field capacity and permanent wilting point. This research, thus, evaluates the performance of three models of support vector regression, artificial neural networks, and linear regression in the development of soil Pedotransfer functions and the effect of number and type of input variables on the performance of the models.


Main Subjects

1-    Baker, L. and Ellison, D., 2008. Optimisation of pedotransfer functions using an artificial neural network ensemble method. Geoderma, 144, pp. 212-224.
2-    Blake, G. and Hartage, K., 1986. Bulk density. In Methods of Soil Analysis, Part Klute. ASA Monogor Madison. Soil Science Society of America,  pp. 363-376.
3-    Botula, Y. D., Nemes, A., Mafuka, P., Van Ranst, E. and Cornelis, W., 2013. Prediction of water retention of soils from the humid tropics by the nonparametric k-nearest neighbor approach. Vadose Zone Journal, 12(2), pp. 1-17.
4-    Chen, S., Yu, P. and Tang, H., 2010. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology, 385, pp. 13-23.
5-    Gee, G. W. and Bauder, J. W., 1986. Particle-size analysis, hydrometer method. In Klute et al. (eds.) Method’s of Soil Analysis Agron. Soil Science Society of America, pp. 404-408.
6-    Hong W., 2011. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm,  Neurocomputing, 74, pp. 2096-2107.
7-    Kaihua, L., Shaohui, X., Jichun ,W., Qing, Z. and N. Lesheng., 2014. Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China. Journal of Plant Nutrition and Soil Science, 177, pp. 775–782.
8-    Kakaeilafdani, E., Moghaddamnia, A. and Ahmadi, A., 2013. Daily suspended sediment load prediction using artificial neural networksand support vector machines. Journal of Hydrology, 478, pp. 50-62.
9-    Kisi, O. and Cimen, A., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399(2), pp. 132-140.
10- Koekkoek, E. J. and Booltink, H., 1999. Neural network models to predict soil water retention. European Journal of Soil Science, 50, pp. 489-495.
11- Lin, G., Chen, G., Huang, P. and Chou, Y., 2009. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. J. of Hydrology, 372, pp. 17-29.
12- MahdaviMeymand, A. and Ahadian, J., 2015. Comparison of Statistical, Experimental, neural network and fuzzy neural network methods in estimation of air Overflow needed. Journal of Irrigation Science and Engineering, 38 (3), pp. 51-61. (In Persian).
13- Minasny, B. and McBratney, A., 2002. The Neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science of Society America, 66, pp. 352–361.
14- Nemes, A., Schaap, M. and Wosten, J., 2003. Functional evaluation of pedotransfer functions derived from different scales of data collection, Journal  of Soil Science,  67, pp. 1093–1102.
15- Nguyen, P.M., De Pue, J., Van, K. L. and Cornelis, W., 2015. Impact of regression methods on improved effects of soil structure on soil water retention estimates, Journal of Hydrology, 29, pp. 598-606.
16- Noori, R., Karbassia, A., Moghaddamniac, D., Hand, M.H., Zokaei-Ashtianie, A., Farokhniab, F. and GhafariGoushehc, M., 2013. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology, 401 (3), pp. 177-189.
17- NikbakhtShahbazi, A., Zahraie, B. and Naseri, M., 2013. Seasonal Meteorological Drought Forecasting Using Support Vector Machines. Journal of Water and Wastewater, 2, pp. 73-85. (In Persian).
18- Shirani, H., 2011. Estimation of some soil moisture characteristic curve points including FC and PWP using soil transfer functions and regression method in Kerman. Journal of Agricultural Science and Technology Soil and Water Sciences, 59 (16), pp. 141-150. (In Persian).
19- Schaap, M. G. and F. Leij., 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil and Tillage Research, 47, pp. 37-42.
20- Shukri, Q., Sadeghi, M. And Ahmadi Marwash, M., 2013. Presentation of a Combined Data Preprocessing Method in Regression Vector Machine to Predict the Quality of Refined Oil. Journal of Petroleum Research, 75 (23), pp. 102-116. (In Persian).
21- Ungaro, F., Calzolari, C. and Busoni, E., 2005. Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano–Veneta region of North Italy. Water Retention Properties Geoderma, 124, pp. 293-317.
22- Vapnik, V. N. and Cortes, C., 1995. Support vector networks. Machine Learning, 20, pp. 273-297.
23- Vali, A., Moiri, M. and Movahediniya, N., 2009. Comparative analysis of artificial neural networks performance and suspended sediment prediction regression models. Natural Geography Research, 71, pp. 21-30. (In Persian).
24- Yin, J. and Log, P., 2011. Prediction for blocked tripe tides with amino acids descriptors (HMLP) by multiple linear regression and support vector regression”, Procedia Environmental Sciences, 8, pp. 173–178.
25- Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O. and Lee, K., 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of  Hydrology,  396 (1), pp. 128-138.
26- JafariGilandeh, S., Khodaverdillo, H. and Rasulzadeh, A., 2017. Application and comparison of parametric transfer functions of Van Genuchten model in simulating unsteady water flow in cultivated soil. Soil Knowledge Journal, 25 (2), pp. 82 - 92. (In Persian).
27- Zhang, Y., 2007. Artificial neural networks based on principal component analysis input selection       for clinical pattern recognition analysis.  Talanta, 73 (1), pp. 68-75.
Volume 42, Issue 4
December 2019
Pages 29-44
  • Receive Date: 04 July 2017
  • Revise Date: 16 December 2017
  • Accept Date: 20 December 2017
  • Publish Date: 22 December 2019