A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

doi:10.1016/j.jhydrol.2016.06.026

Cited by 223 publications

(99 citation statements)

References 64 publications

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“…A number of data-driven modelling studies have demonstrated that monthly streamflow lagged by 1 month (or more) provided some useful information for forecasting at a 1-month lead time (e.g. Bennett et al, 2014;Humphrey et al, 2016;Yang et al, 2017). This study demonstrates that these benefits also hold when CRR models, rather than data-driven approaches, are used as the forecasting model.…”

Section: Beneficial Impact Of State Updating On Forecast Performancementioning

confidence: 67%

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State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Gibbs

McInerney

Humphrey

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

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Section: Beneficial Impact Of State Updating On Forecast Performancementioning

confidence: 67%

“…1) using the statistical downscaling method detailed in Shao and Li (2013). Further details of the downscaling approach are provided in Humphrey et al (2016).…”

Section: Climate Datamentioning

confidence: 99%

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Gibbs

McInerney

Humphrey

et al. 2018

Hydrol. Earth Syst. Sci.

Self Cite

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“…Stedinger et al (1984) found that utilizing a long period of historical data results in more accurate streamflow prediction models. In addition, they showed that adding the snowpack data significantly improves the prediction accuracy [15]. Along with the ANN, hybrid models have been widely used to predict the streamflow and future strategic planning of different resources [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Developing an ANN Based Streamflow Forecast Model Utilizing Data-Mining Techniques to Improve Reservoir Streamflow Prediction Accuracy: A Case Study

et al. 2018

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This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values. Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing datamining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.

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“…While flexible model development approaches go some way towards addressing this problem, the portion of the modelling spectrum they are able 100 to cover remains somewhat limited. In order to allow the relative strengths of different types of models on the hydrological modelling spectrum to be utilised fully, the use of hybrid models has been suggested (Corzo and Solomatine, 2007;Corzo et al, 2009;Robertson & Sharp, 2013;Mount et al, 2016;Humphrey et al, 2016). Such models combine modelling approaches that fall on different points of the modelling spectrum to enable the most appropriate degree of hypothetic and data influence to be utilised in the modelling of each sub-process, given the intended purpose of the model, the degree of understanding of a 105 particular process, and the type and amount of data available to support model development.…”

Section: Introduction 35mentioning

confidence: 99%

Modelling salinity in river systems using hybrid process and data-driven models

Hunter

Maier

Gibbs

et al. 2017

Preprint

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Abstract. Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process-or data-driven. The primary problem with process-10 based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically, and that there are often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs), is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a hybrid modelling approach is introduced and applied in this 15paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the River Murray in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, Hydrol. Earth Syst. Sci. Discuss., https://doi

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A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

Cited by 223 publications

References 64 publications

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Developing an ANN Based Streamflow Forecast Model Utilizing Data-Mining Techniques to Improve Reservoir Streamflow Prediction Accuracy: A Case Study

Modelling salinity in river systems using hybrid process and data-driven models

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