Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

Soil moisture (SM) is a key component of the global energy cycle that regulates all domains of the natural environmental and the agricultural system. In this research, the challenge is to develop a low-cost data-intelligent SM forecasting model using climate dynamics (i.e., the climate indices, atmospheric and hydro-meteorological parameters) as the model inputs. A newly designed, multi-model ensemble committee machine learning approach based on the artificial neural network (ANN-CoM) is developed to forecast monthly upper layer (∼0.2 m from the surface) and the lower layer (∼0.2–1.5 m deep) SM at four agricultural sites in Australia’s Murray-Darling Basin. ANN-CoM model is validated with respect to non-tuned second-order Volterra, M5 model tree, random forest, and an extreme learning machine (ELM) models. To construct the ANN-CoM model, the input variables comprised of the hydro-meteorological data from the Australian Water Availability Project, large-scale climate indices and atmospheric parameters derived from the Interim ERA European Centre for Medium-Range Weather Forecasting ECMWF reanalysis fields leads to a total of 60 potential predictors used for SM forecasting. To reduce the model input data dimensionality for accurate forecasts, the Neighborhood Component Analysis (NCA) based feature selection algorithm for regression purposes (fsrnca) is applied to determine the relative feature weights related to the targeted variable. The optimal predictor variables are then screened with an ELM model as the fitness function of the fsrnca algorithm to identify the set of most pertinent model variables. Extensive performance evaluation using statistical score metrics with visual and diagnostic plots show that the ensemble committee based, ANN-CoM model is able to effectively capture the nonlinear dynamics involved in the modeling of monthly upper and lower layer SM levels. Therefore, the ANN-CoM multi-model ensemble-based approach can be considered to be a superior SM forecasting tool, portraying as an amicable, integrated (or ensemble) machine learning stratagem that can be explored for soil moisture modeling and applications in agriculture and other hydro-meteorological phenomena.

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