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Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy. • We predict groundwater levels based on stream flow and rainfall data. • Our framework uses Bayesian data imputation and deep learning for multi-step prediction. • We examine the groundwater fluctuations at various boreholes located in Australia. • Results show a fine-tuned regional model with rainfall data yielded the best accuracy. • We find that LSTM models outperformed CNNs using the multivariate approach. [ABSTRACT FROM AUTHOR]

Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation. • Spatial predictions of wildfire spread are frequently used but not always evaluated. • We synthesised fifteen loss functions for evaluating predicted vs observed wildfire spread. • There is not a 'best' loss function, it depends on simulation use and interpretation. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]

Despite the considerable success of deep learning methods in modelling physical processes, they suffer from a variety of issues such as overfitting and lack of interpretability. In hydrology, conceptual rainfall-runoff models are simple yet fast and effective tools to represent the underlying physical processes through lumped storage components. Although conceptual rainfall-runoff models play a vital role in supporting decision-making in water resources management and urban planning, they have limited flexibility to take data into account for the development of robust region-wide models. The combination of deep learning and conceptual models has the potential to address some of the aforementioned limitations. This paper presents a sub-model hybridization of the GR4J rainfall-runoff model with deep learning architectures such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The results show that the hybrid models outperform both the base conceptual model as well as the canonical deep neural network architectures in terms of the Nash–Sutcliffe Efficiency (NSE) score across 223 catchments in Australia. We show that our hybrid model provides a significant improvement in predictive performance, particularly in arid catchments, and generalizing better across catchments. • Hybrid modelling features parsimony of data-driven and conceptual hydrologic models. • We present a deep learning-based hybridization for the GR4J rainfall-runoff model. • We present a hierarchical training approach consisting of gradient-based and evolutionary optimization of the respective models. • The results show that hybrid models outperform the canonical (base) hydrologic and deep learning model. • The addition of surface temperature and antecedent streamflow further improves the predictive performance. [ABSTRACT FROM AUTHOR]

Abstract Computer modelling and design tools can assist in environmental management. In particular, post-mining landscapes with large volumes of materials require shaping for optimal erosional stability and ecological and visual integration into the surrounding undisturbed landscape. This paper evaluates the complementary capabilities of landscape evolution modelling (SIBERIA) and geomorphic design software (Natural Regrade with GeoFluv). An existing 11.5-ha waste rock dump (Hunter Valley, New South Wales, Australia) served as the study site. The SIBERIA modelling demonstrated that geomorphic design reduced erosion by half that of conventional designs while being able to store an extra 7% of mine waste volume. Additionally, the spatial pattern of gullying was able to be predicted by modelling, which allowed management in subsequent geomorphic design, and successively more stable patterns. In conclusion, the joint use of the Natural Regrade with GeoFluv geomorphic design software with the SIBERIA landscape evolution model showed complementary capabilities for enhancing mine rehabilitation. Highlights • Computer models and modelling design greatly improve mine site rehabilitation. • We demonstrate the SIBERIA and Natural Regrade with GeoFluv software. • The complementary tools reduce erosion yet can increase landscape volume. [ABSTRACT FROM AUTHOR]

Fire behaviour and spread predictions guides suppression strategies and public warnings during wildfires. The scientific understanding of fire behaviour forms the core of these predictions, but is incomplete, and expert judgement and experience are required to augment the evidence based knowledge. Amicus is a new decision support system that implements contemporary, published and operationalised bushfire behaviour models (e.g. rate of spread, flame height, fireline intensity, spotting distance) in the Australian bushfire context. It enables the inclusion of expert judgement and local knowledge, allows users to analyse temporal trends and uncertainty in inputs, and facilitates reliable and practical predictions. This paper provides a comprehensive overview of Amicus, including its operation and functionality, identifies the boundaries of the current understanding of fire science, discusses the major limitations in existing knowledge, and provides a framework for allowing deterministic and anecdotal/local knowledge to be incorporated into formal fire behaviour predictions. [ABSTRACT FROM AUTHOR]

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages in geological timescales. Weencounter multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use a synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of the parameters in pyReef-Core , which provides the groundwork for future research in this area. • Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. • pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes. • This paper presents Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core. • The results show that Bayesreef accurately estimates and provides uncertainty quantification for parameters in pyReef-Core. • Bayesreef provides insights in of pyReef-Core and establishes the groundwork for future research. [ABSTRACT FROM AUTHOR]