Document Type : Research Paper


1 M.Sc. Alumni, Department of Irrigation and Drainage Engineering, Abouraihan Campus, University of Tehran, Tehran, Iran

2 M.Sc. Alumni, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran


Predicting the amount of soil exchange capacity is very valuable because it is a key indicator of soil quality for nutrient storage. In this research, using a neural-fuzzy network (ANFIS), the soil exchange capacity value was predicted with the input parameters of soil properties (such as clay, sludge, sand, gypsum and organic matter). The study area was considered in the northwest of Iran, where about 380 samples were taken from different areas. Of these samples, about 75% of the data were considered for training and 25% of the data were considered for testing. According to the number of different inputs, about 6 models of the combination of input parameters were developed.  To improve the prediction results, the Harris hawk optimizer (HHO) algorithm was used for ANFIS training. The pattern results of each model were analyzed using evaluation indices such as root mean square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (CC), Williams index (WI), and Taylor diagram. The results showed that the input pattern P6, which includes all input parameters, had the highest accuracy in predicting CEC. In this pattern, the error values of CC, WI, MAPE and RMSE for the test data were 0.90, 0.75, 0.11 and 1.89 cmol /kg, respectively. The results of Taylor diagram also indicated the appropriate accuracy of the pattern so that the CEC can be predicted with appropriate accuracy. In general, the prediction error was reduced by about 1.3 to 2 cmol/kg using the HHO algorithm.


Main Subjects

Azar, N. A., Milan, S. G. and Kayhomayoon, Z. (2021). The prediction of longitudinal dispersion coefficient in natural streams using LS-SVM and ANFIS optimized by Harris hawk optimization algorithm. J. Contam. Hydrol., 103781.
Bui, D. T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A. S. A. (2019). A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility. J. Sensors, 19(16), 3590.
Czarnecki, S. and Düring. R. A. (2015). Influence of long-term mineral fertilization on metal contents and properties of soil samples taken from different locations in Hesse, Germany. J. Soil, 1(1), 23-33.
Essa, F. A., Abd Elaziz, M. and Elsheikh, A. H. (2020). An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer. Appl. Therm. Eng., 170, 115020.
Gruszczynski, S. (2009). Assessment of suitability of various models for estimating cation exchange capacity (CEC). Pol. J. Soil. Science. 42(1). 16-29.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Gener. Comput. Syst., 97. 849-872. Doi: 10.1016/ J. future.2019.02.028.
Jafarzadeh, A. A., Pal, M., Servati, M., FazeliFard, M. H. and Ghorbani, M. A. (2016). Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction. Int. J. Environ. Sci. Technol., 13(1). 87-96.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man. Cybernet., 23(3), 665-685.
Kvalheim, O. M. (2010). Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots. J. Chemomet., 24(7‐8). 496-504.
Liao, K., Xu, S., Wu, J., Zhu, Q. and An, L. (2014). Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China. J. Plant Nut. Soil Sci., 177(5), 775-782.
Milan, S. G., Roozbahani, A., Azar, N. A. and Javadi, S. (2021). Development of adaptive neuro fuzzy inference system–evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation. J. Hydrol., 598, 126258.
Moayedi, H., Gör, M., Lyu, Z. and Bui, D. T. (2020). Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient. Measur., 152. 107389.
Pachepsky, Y. A., Timlin, D. and Varallyay, G. Y. (1996). Artificial neural networks to estimate soil water retention from easily measurable data. J. Soil Sci. Soc. Am., 60(3), 727-733.
Parker, R. (2009). Plant & soil science: Fundamentals & applications. Cengage Learning.
Sammen, S. S., Ghorbani, M. A., Malik, A., Tikhamarine, Y., AmirRahmani, M., Al-Ansari, N. and Chau, K. W. (2020). Enhanced artificial neural network with harris hawks optimization for predicting scour depth downstream of ski-jump spillway. J. Appl. Sci., 10(15), 5160.
Sharafati, A., Haghbin, M., Aldlemy, M. S., Mussa, M. H., Al Zand, A. W., Ali, M. and Yaseen, Z. M. (2020). Development of advanced computer aid model for shear strength of concrete slender beam prediction. J. Appl. Sci., 10(11), 3811.
Shehabeldeen, T. A., Abd Elaziz, M., Elsheikh, A. H. and Zhou, J. (2019). Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with Harris hawks optimizer. J. Mat. Res. Technol., 8(6). 5882-5892.
Tang, L., Zeng, G., Nourbakhsh, F. and Shen, G. L. (2009). Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties. J. Environ. Eng. Sci., 26(1), 137-146.
Tikhamarine, Y., Souag-Gamane, D., Ahmed, A. N., Sammen, S. S., Kisi, O., Huang, Y. F. and El-Shafie, A. (2020). Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization. J. Hydrol., 589, 125133.
Xu, S., Zhao, Y., Wang, M. and Shi, X. (2018). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma. 310. 29-43.