Publication date: 15 November 2018
Source:Geoderma, Volume 330
Author(s): Ramendra Prasad, Ravinesh C. Deo, Yan Li, Tek Maraseni
Soil moisture (SM ) is an essential component of the environmental and the agricultural system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to understand the soil dynamics for proactive planning and decision-making measures for agriculture and related fields. In this study hybrid data-intelligent, extreme learning machine (ELM) models are designed and explored for monthly SM forecasting. The chaotic, complex and dynamical behavior of SM can compound the accuracy of data-driven models. Consequently, two versatile, computationally efficient and self-adaptive multi-resolution utilities namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the ensemble empirical mode decomposition (EEMD) algorithms are utilized to address these data non-stationarity issues, which if not resolved can lead to model prediction inaccuracies. The difference in these approaches is that, during the EEMD process, a Gaussian white noise is added to the intact (i.e. , unresolved) time series only, while, the CEEMDAN requires sequential additions at each decomposition phase. Integration of these multi-resolution tools with the ELM model led to the hybrid CEEMDAN-ELM and the EEMD-ELM models, that were benchmarked with random forest (RF) equivalent models. Using WaterDyn model's hind-simulated SM data, these models were applied (without any climate inputs) to forecast the upper (0.2 m) and the lower layer (0.2–1.5 m depth) soil moisture in Australia's agricultural-hub, the Murray-Darling Basin. The standalone ELM and RF model has similar computation efficiency and model performances. However, despite the implementation of computationally expensive ensemble techniques (i.e. , EEMD and CEEMDAN, the hybrid ensembles EEMD-ELM and CEEMDAN-ELM were highly efficient with improved performances. The research outcomes showed that the CEEMDAN-ELM model outperformed the alternative models at three (out of the seven) sites applied for upper layer SM forecasts, while the EEMD-ELM hybrid model was superior at all seven sites for the lower layer soil moisture forecasts. The study signifies the important role of the self-adaptive multi-resolution utility (CEEMDAN) hybridized with the ELM algorithm to potentially develop automated prediction systems for forecasting soil moisture, with potential applications in agriculture.
Source:Geoderma, Volume 330
Author(s): Ramendra Prasad, Ravinesh C. Deo, Yan Li, Tek Maraseni
