Evaluation of Different Supervised Learning Smart Methods and Response Surface Method to Optimize Factors Affecting Erosion (Case Study: Emamzadeh Watershed of Baghmalek)

Document Type : Research Paper


1 shahid chamran university of ahvaz

2 Department of Biosystem Faculty of Agriculture Shahid Chamran University of Ahvaz


Evaluation of soil erosion control factors is important regarding the application of management practices. In this study, the effects of non-structural management practices including revision of crop cover (RC) and exclosure (EX) were simulated using WEPP model in ​​Emamzadeh Abdullah watershed of Baghmalek, located in the northeast of Khuzestan Province. Optimization of physical and hydraulic parameters affecting erosion including soil texture and components of the Van Genuchten equation was performed using response surface methodology (RSM), random forest (RF), support vector machine (SVM) and artificial neural network (ANN). Also, the soil erosion rate before and after management practices was defined as the first response (R1) and the second response (R2), respectively. Optimization results by Orange software including random forest methods, support vector machine and artificial neural network showed that the random forest method with MSE, RMSE and R2 equal to 0.991, 0.995 and 0.963 respectively, for the first response and equal to 0.095, 0.307 and 0.974 respectively, for the second response is the most proper method. Also, optimization by response surface method is the most appropriate method with MSE, RMSE and R2 equal to 28.7, 5.37 and 0.999 respectively, for the first response and equal to 4.16, 2.03 and 0.998 respectively, for the second response. Generally, using optimization techniques is a convenient method for evaluating management practices and finally selecting the best one for critical areas. Appropriate management practices based on optimal conditions leading to water and soil loss reduction.


Main Subjects

Andersen, P. S., Andersen, E., Graversgaard, M., Christensen, A. A., Vejre, H., and Dalgaard, T. (2019). Using landscape scenarios to improve local nitrogen management and planning. Journal of Environmental Management, 232, 523–530.
Azimi-Pour, M., Eskandari-Naddaf, H., and Pakzad, A. (2020). Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construction and Building Materials, 230: 117021.
Bagdi, G. L., Mishra, P. K., Kurothe, R. S., Arya, S. L., Patil, S. L., Singh, A. K., …and Sundarambal, P. (2015). Post-adoption behaviour of farmers towards soil and water conservation technologies of watershed management in India. International Soil and Water Conservation Research, 3(3): 161–169.
Baigorria, G. A., and Romero, C. C. (2007). Assessment of erosion hotspots in a watershed: Integrating the WEPP model and GIS in a case study in the Peruvian Andes. Environmental Modelling & Software, 22(8): 1175–1183.
Booker, D. J., and Snelder, T. (2012). Comparing methods for estimating flow duration curves at ungauged sites. Journal of Hydrology, 434: 78-94.‏
Bouyucos, C.J. (1962). Hydrometer method improved for making particle-size analysis of soil. Journal of Agrometer, 54: 464-465.
Box, G. E. P., and Draper, N. R. (2007). Response surfaces, mixtures and ridge analyses, John Wiley and Sons, New York, 857.
Brady, N.C. and Weil, R.R. (2014). The Nature and Properties of Soils, Revised 14th edition. Pearson, Prentice Hall, Upper Saddle River, New Jersey and Columbus, Ohio, USA (pp 975).
Breiman, L. (2001). Random forests. Machine learning, 45(1): 5-32.
Chen, G., Wang, Y., Li, S., Cao, W., Ren, H., Knibbs, L. D and Guo, Y. (2018). Spatiotemporal patterns of PM10 concentrations over China during 2005–2016: a satellite-based estimation using the random forests approach. Environmental pollution, 242: 605-613.
Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3): 273-297.
Demšar, J., and Zupan, B. (2013). Orange: Data mining fruitful and fun-a historical perspective. Informatica, 37(1): 56-50.
Eggers, J., Holmgren, S., Nordström, E.-M., Lämås, T., Lind, T., and Öhman, K. (2017). Balancing different forest values: Evaluation of forest management scenarios in a multi-criteria decision analysis framework. Forest Policy and Economics, 103, 55-69.
Heung, B., Bulmer, C. E., and Schmidt, M. G. (2014). Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma, 214: 141-154.
Lal, R. (2019). Accelerated soil erosion as a source of atmospheric CO2. Soil and Tillage Research, 188: 35-40.‏
Naik, A., and Samant, L. (2016). Correlation review of classification algorithm using data mining tool: WEKA, Rapidminer, Tanagra, Orange and Knime. Procedia Computer Science, 85: 662-668.‏
Nath, A. J., and Lal, R. (2017). Managing tropical wetlands for advancing global rice production: Implications for land-use management. Land Use Policy, 68: 681-685.
‏ Nezhadafzali, K., Shahrokhi, M., and Bayatani, F. (2019). Assessment soil erosion using RUSLE model and identification the most effective factor in Dekhan watershed basin of southern Kerman. Journal of Natural Environmental Hazards, 20 (8): 21-38.
Norouzi, H., Azgharimoghaddam, A., and Nadiri, A. (2019). Determination of vulnerable areas of Dasht-e malekan watershed to nitrate using random forest method. Ecology, 41(4):923-942.
Oehlert, W. Gary. (2000). Design and analysis of experiments: Response surface design, New York: W.H. Freeman and Company.
Parsafar, N and Marofi, S. (2011). Estimation of soil temperature from air temperature using regression models, artificial neural network and adaptive neuro-fuzzy inference system (Case Study: Kermanshah Region). Journal of Water and Soil Science, 21(4): 141-152. (In Farsi).
Partopour, B., Paffenroth, R. C., and Dixon, A. G. (2018). Random forests for mapping and analysis of microkinetics models. Computers & Chemical Engineering, 115: 286-294.
Podvalny, S. L., and Vasiljev, E. M. (2017). The principle of multi-alternativity in intelligent systems. Active neural network models. Procedia Computer Science, 103: 410-415.
Raymond, H, M., Douglas C, Montgomery and Christine M, Anderson-cook. (2009). Response surface methodology. Published by John Wiley & Sons, Inc., Hoboken, New Jersey published simultaneously in Canada, 705.
Rouzies, E., Lauvernet, C., Barachet, C., Morel, T., Branger, F., Braud, I., and Carluer, N. (2019). From agricultural catchment to management scenarios: A modular tool to assess effects of landscape features on water and pesticide behavior. Science of the Total Environment, 671, 1144–1160.
Shirazi, M., Khadealrasoul, A., and Saffieddin Ardebili, S. M. (2019). Evaluation of hydraulic parameters on water erosion using response surface methodology (RSM). 16th Iranian Soil Science Congress, University of Zanjan.
Shirazi, M., Khademalrasoul, A. & Safieddin Ardebili, S.M. (2020). Multi-objective optimization of soil erosion parameters using response surface method (RSM) in the Emamzadeh watershed. Acta Geophys. 68, 505–517 (2020). https://doi.org/10.1007/s11600-020-00404-5.
Shirmohammadi, B., Malekian, A., Salajegheh, A., Taheri, B., Azarnivand, H., Malek, Z., and Verburg, P. H. (2020). Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran. Land Use Policy, 90, 104299.
Sun, L., Zou, B., Fu, S., Chen, J., and Wang, F. (2019). Speech emotion recognition based on DNN-decision tree SVM model. Speech Communication, 115: 29-37.
Thomas, G. W. (1982). “Exchangeable Cations. Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties”, Second Edition. A.L. Page (editor). Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, WL: 159-165.
Tripathi, R., Nayak, A. K., Shahid, M., Lal, B., Gautam, P., Raja, R. and Sahoo, R. N. (2015). Delineation of soil management zones for a rice cultivated area in eastern India using fuzzy clustering. Catena, 133: 128-136.‏
Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.‏
Vorpahl, P., Elsenbeer, H., Märker, M., and Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239: 27-39.
‏Walkley, A. and Black, I.A. (1934). An examination of the degtjareff method for determining soil organice matter and proposed modification of chromic acid titration method. Soil SCIENCE, 37: 29-38.
Wilson, M. J. (2019). The importance of parent material in soil classification: A review in a historical context. Catena, 182: 104131.
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., and Lee, K. 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(1-2): 128-138.