[1] There has been a recent debate in the hydrological community about the relative merits of the informal generalized likelihood uncertainty estimation (GLUE) approach to uncertainty assessment in hydrological modeling versus formal probabilistic approaches. Some recent literature has suggested that the methods can give similar results in practice when properly applied. In this note, we show that the connection between formal Bayes and GLUE is not merely operational but goes deeper, with GLUE corresponding to a certain approximate Bayesian procedure even when the ''generalized likelihood'' is not a true likelihood. The connection we describe relates to recent approximate Bayesian computation (ABC) methods originating in genetics. ABC algorithms involve the use of a kernel function, and the generalized likelihood in GLUE can be thought of as relating to this kernel function rather than to the model likelihood. Two interpretations of GLUE emerge, one as a computational approximation to a Bayes procedure for a certain ''error-free'' model and the second as an exact Bayes procedure for a perturbation of that model in which the truncation of the generalized likelihood in GLUE plays a role. The intent of this study is to encourage cross-fertilization of ideas regarding GLUE and ABC in hydrologic applications. The connection we outline suggests the possibility of combining a formal likelihood with a kernel based on a generalized likelihood within the ABC framework and also allows advanced ABC computational methods to be used in GLUE applications. The model-based interpretation of GLUE may also be helpful in partially illuminating the implicit assumptions in different choices of generalized likelihood.

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Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)–ensemble empirical mode decomposition (EEMD)–bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD–BiLSTM model outperforms all other models with a higher Pearson’s correlation (R) value of 0.9961 (BiLSTM—0.991, EEMD-support vector regression (SVR)—0.9852, SVR—0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM—0.0248, EEMD-SVR—0.043, SVR—0.0507) for Cairns and similarly a higher Pearson’s correlation (R) value of 0.9965 (BiLSTM—0.9903, EEMD–SVR—0.9953, SVR—0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM—0.075, EEMD-SVR—0.0481, SVR—0.057) for Gold Coast.

Work-based placements, site visits, field trips and embedded industry-informed curriculum are employability strategies frequently applied by universities, and clustered under the umbrella term – work-integrated learning (WIL). Referring to each of these strategies as WIL can complicate comparisons (e.g. long-term placements vs. field trips) and can lead WIL related research to diverge in multiple directions. To support comparison and help guide institutional decision-making relating to WIL, the positioning of this article aligns with a recent stream of literature that attempts to outline, contrast and differentiate between various activities aimed at enhancing graduate employability. Four distinct WIL case studies from three Australian universities are described in this article: (a) students working in teams with industry partners (n=23), (b) students co-creating learning resources (n=7), (c) a student-staff partnership (n=2), and (d) students acting as peer-learning advisors (n=5). The cases were considered across five key factors: 1) ease of implementation, 2) barriers, 3) scalability, 4) authenticity, and 5) proximity. Using empirical data, the findings within the article contribute an institutional framework that highlights the benefits and drawbacks associated with differences across WIL types, intended to support good WIL practice among administrators, teachers and staff.