Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration

Abstract

Yubin, Z., Zhengying, W., Lei, Z., Qinyin, L., & Jun, D. (March-April, 2017). Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration. Water Technology and Sciences (in Spanish), 8(2), 127-140. The traditional extreme learning machine has significant disadvantages, including slow training, difficulty in selecting parameters, and difficulty in setting the singularity and the data sample. A prediction model of an improved Online Sequential Extreme Learning Machine (IOS-ELM) of daily reference crop evapotranspiration is therefore examined in this paper. The different manipulation of the inverse of the matrix is made according to the optimal solution and using a regularization factor at the same time in the model. The flexibility of the IOS-ELM in ET0 modeling was assessed using the original meteorological data (Tmax, Tm, Tmin, n, Uh, RHm, φ, Z) of the years 1971–2014 in Yulin, Ankang, Hanzhong, and Xi’an of Shaanxi, China. Those eight parameters were used as the input, while the reference evapotranspiration values were the output. In addition, the ELM, LSSVM, Hargreaves, Priestley-Taylor, Mc Cloud and IOS-ELM models were tested against the FAO- 56 PM model by the performance criteria. The experimental results demonstrate that the performance of IOS-ELM was better than the ELM and LSSVM and significantly better than the other empirical models. Furthermore, when the total ET0 estimation of the models was compared by the relative error, the results of the intelligent algorithms were better than empirical models at rates lower than 5%, but the gross ET0 empirical models mainly had 12% to 64.60% relative error. This research could provide a reference to accurate ET0 estimation by meteorological data and give accurate predictions of crop water requirements, resulting in intelligent irrigation decisions in Shaanxi.