New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes

Abstract

Reference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems. A novel multivariate variational mode decomposition integrated with a boosted regression tree (i.e., MVMD-BRT) is constructed to forecast daily ETo. Firstly, the correlation matrix based on cross-correlation was computed to investigate the significant input predictor lags of daily ETo. Secondly, the MVMD technique decomposes the significant input lags into signals called intrinsic mode functions (IMFs). Thirdly, the IMFs were then employed in the BRT to build the MVMD-BRT model for daily ETo forecasting. A comparative assessment of MVMD against multivariate empirical mode decomposition (MEMD) was also performed on the same lines to develop the MEMD-BRT model. The MVMD-BRT model is compared against the random forest (RF) and hybrid MVMD-RF, MEMD-RF, extreme learning machine (ELM), and hybrid MVMD-ELM, MEMD-ELM, and cascaded feedforward neural network (CFNN) along with its hybrid MVMD-CFNN models for two stations in Queensland, Australia using a set of goodness-of-fit metrics. The results prove that the MVMD-BRT provide accurate daily ETo forecasting against the benchmark models. The MVMD-BRT model yielded the highest accuracy in terms of (WIE = 0.9070, NSE = 0.8421, LME = 0.6529, KGE = 0.8792) and (WIE = 0.8966, NSE = 0.8396, LME = 0.6521, KGE = 0.8803) for Brisbane and Gympie stations against the comparing models.

Keywords

• Reference evapotranspiration
• Multivariate variational mode decomposition
• Multivariate empirical mode decomposition
• Boosted regression tree