Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

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Vol17 No.7: 1712-1723

Title】Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Author】Hojat GHANJKHANLO1; Mehdi VAFAKHAH1*; Hossein ZEINIVAND2; Ali FATHZADEH3

Addresses】1 Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor 64414-356, Iran; 2 Department of Watershed Management Engineering, Lorestan University, Khoramabad, 68151-44316, Iran; 3 Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, 89518-95491, Iran

Corresponding author】Mehdi VAFAKHAH

Citation】Ghanjkhanlo H, Vafakhah M, Zeinivand H, et al. (2020) Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid Region of Iran. Journal of Mountain Science 17(7). https://doi.org/10.1007/s11629-018-4875-8

DOI】https://doi.org/10.1007/s11629-018-4875-8

Abstract】Direct measurement of snow water equivalent (SWE) in snow-dominated mountainous areas is difficult, thus its prediction is essential for water resources management in such areas. In addition, because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution, statistical models are not usually able to present acceptable results. Therefore, applicable methods that are able to predict nonlinear trends are necessary. In this research, to predict SWE, the Sohrevard Watershed located in northwest of Iran was selected as the case study.Database was collected, and the required maps were derived. Snow depth (SD) at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling (LHS), and snow density at 18 points were randomly measured, and then SWE was calculated. SWE was predicted using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and regression methods. The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method. Moreover, based onmost of the efficiency criteria, the efficiency of ANN, ANFIS and regression methods under LHSpattern were observed higher than the systematic random sampling pattern. However, there were no significant differences between the two methods of ANN and ANFIS in SWE prediction. Data of both two sampling patterns had the highest sensitivity to the elevation. In addition, the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature, respectively.

Keywords】ANFIS;ANN;Latin hypercube sampling;Systematic random sampling;Snow water equivalent; Snow depth