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

Prasad, Ramendra and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 and Maraseni, Tek ORCID: https://orcid.org/0000-0001-9361-1983 (2018) Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil and Tillage Research, 181. pp. 63-81. ISSN 0167-1987


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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. This study was supported by University of Southern Queensland Office of Research and Graduate Studies (ORGS) Postgraduate Research Scholarship (USQ-PRS) Grant # 2016 awarded to the first author, supervised by Dr Ravinesh Deo
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment (1 Aug 2018 -)
Date Deposited: 30 Apr 2018 02:00
Last Modified: 11 Jul 2022 03:35
Uncontrolled Keywords: committee of models; extreme learning machine; random forest; Volterra; M5 tree; soil moisture forecasting; Murray-Darling Basin
Fields of Research (2008): 04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040608 Surfacewater Hydrology
05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
Fields of Research (2020): 37 EARTH SCIENCES > 3707 Hydrology > 370704 Surface water hydrology
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring
37 EARTH SCIENCES > 3702 Climate change science > 370202 Climatology
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
D Environment > 96 Environment > 9610 Natural Hazards > 961003 Natural Hazards in Farmland, Arable Cropland and Permanent Cropland Environments
Identification Number or DOI: https://doi.org/10.1016/j.still.2018.03.021
URI: http://eprints.usq.edu.au/id/eprint/34058

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