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LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios


Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.

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Augmented Dickey–Fuller


Artificial neural network


Fifth assessment report


Boruta-random forest hybridizer algorithm


Two-stage hybrid model integrating the Boruta feature selection algorithm and significant lagged memory with LSTM


Two-stage hybrid model integrating the Boruta feature selection algorithm and significant lagged memory with SVR


Two-stage hybrid model integrating the Boruta feature selection algorithm and significant lagged memory with MARS


Centre for Environmental Data Analysis


Coupled Model Inter-comparison Project Phase 5


Convolutional neural network


Commonwealth Scientific and Industrial Research Organization


Deep learning


Extreme learning machine


Empirical mode decomposition


Feed forward neural networks


Global Climate Models


Intergovernmental Panel on the Climate Change


Kling–Gupta efficiency


Legates–McCabe's index


Long-short term memory


Mean absolute error


Mean absolute percentage error


Murray–Darling basin


Multivariate empirical mode decomposition


Multi-layer perceptron


Mean squared error


Nash–Sutcliffe efficiency


Partial autocorrelation function




Correlation coefficient




Recurrent neural network


Relative root-mean-square error


Standard deviation


Soil moisture


Support vector regression


Willmott's Index of Agreement


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The authors would like to recognise the funding contribution of the Chinese Academy of Science (CAS) and the University of Southern Queensland (USQ) to provide a USQ-CAS postgraduate research scholarship (2019–2021) awarded to the first author, managed by the USQ Graduate Research School. The authors are greatful to the Editor and Reviewers whose comments have imporved the clarity of the final paper.

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A.A.M.A.: Writing—original draft, Conceptualisation, Methodology, Software, Model development and application. R.C.D.: Conceptualisation, Writing—review and editing, Investigation, Supervision. A.G.: Conceptualisation, Writing—review and editing. N.R.: Writing—review and editing, Q.F.: Writing—review and editing. Z.Y.: Writing—review and editing, L.Y.: Writing—review and editing.

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Correspondence to A. A. Masrur Ahmed or Ravinesh C. Deo.

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Ahmed, A.A.M., Deo, R.C., Ghahramani, A. et al. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stoch Environ Res Risk Assess 35, 1851–1881 (2021).

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  • Soil moisture
  • Hybrid model
  • Boruta-random forest optimiser algorithm (BRF)
  • Global climate model (GCM)
  • Murray darling basin
  • Long short-term memory (LSTM)