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Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

Xiaona Zhang1,*, Jie Feng2, Zhen Hong3, Xiaona Rui4

1 School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Water Resources Research Institute, China Institute of Water Resources and Hydropower Research, Beijing, 100044, China
3 Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, 73071, USA
4 Wuxi Research Institute, Nanjing University of Information Science & Technology, Wuxi, 214100, China

* Corresponding Author: Xiaona Zhang. Email: email

Computer Systems Science and Engineering 2022, 42(2), 677-688.


In regards to soil macropores, the solute loss carried by overland flow is a very complex process. In this study, a fuzzy neural network (FNN) model was used to analyze the solute loss on slopes, taking into account the soil macropores. An artificial rainfall simulation experiment was conducted in indoor experimental tanks, and the verification of the model was based on the results. The characteristic scale of the macropores, the rainfall intensity and duration, the slope and the adsorption coefficient of ions, were chosen as the input variables to the Sugeno FNN model. The cumulative solute loss quantity on the slope was adopted as the output variable of the Sugeno FNN model. There were three membership functions, and the type of membership function was gbellmf (generalized bell membership function). The hybrid learning algorithm, which combines the back propagation algorithm with a least square method, was applied to train and optimize the network parameters, and the optimal network parameters were determined. The simulation results showed that the simulated values were consistent with the measured values.


Cite This Article

X. Zhang, J. Feng, Z. Hong and X. Rui, "Modelling of the slope solute loss based on fuzzy neural network model," Computer Systems Science and Engineering, vol. 42, no.2, pp. 677–688, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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