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Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Reference crop evapotranspiration (ETo) is an integral hydrological factor in soil–plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning approach, combining convolutional neural network (CNN) and gated recurrent unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ETo forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived moderate resolution imaging spectroradiometer, ground-based datasets from scientific information for landowners and synoptic-scale climate indices. To develop a vigorous CNN-GRU model, a feature selection stage entails the ant colony optimization method implemented to improve the ETo forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ETo. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model's viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model's utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management. [ABSTRACT FROM AUTHOR]

[Display omitted] • We built hybrid Long Short-Term Memory model for Pan Evaporation (E p). • We adopt Neighbourhood Component Analysis for feature selection. • We predict E p in drought-prone Queensland with satellite predictor variables. • Our NCA-LSTM model is compared with competitive benchmark models. • NCA-LSTM model is useful for estimation of evaporative water losses. Evaporation, as a core process within the global hydrological cycle, requires reliable methods to monitor its variation, for decision-making in agriculture, irrigation systems and dam operations, also in other areas of hydrology and water resource management. Accurate monitoring of pan evaporation (E p) is one the most popular approaches to understand the evaporative process. This work aims to construct a hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Neighbourhood Component Analysis for feature selection to predict E p in drought-prone regions in Queensland, Australia (Amberley, Gatton, Oakey, & Townsville). Utilizing the daily-scale dataset [31 August 2002 to 22 September 2020], the performance of the proposed deep learning (DL) hybrid model, denoted as NCA-LSTM, is compared with competitive benchmark models, i.e., standalone LSTM, other types of DL, single hidden layer neuronal architecture and decision tree-based method. The testing results reveal the lowest Relative Root Mean Square Error ≤ 20 % , Absolute Percentage Bias ≤ 14.5 % and the highest Kling-Gupta Efficiency ≥ 87 % attained by the NCA-LSTM hybrid model (relative to benchmark models) tested for Amberley, Gatton, and Oakey sites. In respect to the predictive efficiency, the proposed NCA-LSTM hybrid model, improved with feature selection, outperforms all benchmark models, indicating its future utility in the prediction of daily E p. In practical sense, the predictive model developed for E p estimation provides an accurate estimation of evaporative water loss in hydrological cycle and therefore, can be implemented in areas of irrigation management, planning of irrigation-based agriculture, and mitigation of financial losses to agricultural and related sectors where, regular monitoring and forecasting of water resources are a vital part of sustainable livelihood and business. [ABSTRACT FROM AUTHOR]

Modelling workflow for BRF-LSTM and BRF-GRU predictive models. [Display omitted] • Deep learning hybrid predictive model (BRF-LSTM) is built for streamflow forecasting. • Climate indices, rainfall and periodicity are factored for accurate predictions. • Adopts feature algorithm (i.e. Boruta) with long short-term memory and gated recurrent unit. • Six gauging sites tested; BRF-LSTM yields > 98% errors in ± 0.015 m, ~1.30% relative error. • The proposed model is useful for hydrological and strategic water resources planning. Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level. [ABSTRACT FROM AUTHOR]