Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters

Karri, Rama Rao; Badwe, Abhijit; Wang, Xuan; Serafy, Ghada El; Sumihar, Julius; Babovic, Vladan; Gerritsen, H.

doi:10.1007/s10236-012-0584-y

Cited by 24 publications

(4 citation statements)

References 27 publications

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“…The model was calibrated using OpenDA and through comparison of the simulated water level values with values at the Heysham tidal station (https://ntslf.org/data/uk-network-real-time). The model was calibrated using OpenDA (Carnacina, Lima Rego, Verlaan, Zijl, & Van der Kaaij, 2015;Karri et al, 2013;Kurniawan, Ooi, Hummel, & Gerritsen, 2011; "OpenDA: Integrating models and observations,"). OpenDA interfaces with Delft3D and uses a derivative free algorithm (DUD or doesn't use derivative, Ralston and Jennrich, 1978), an algorithm for non-linear least squares minimization, to minimize a quadratic cost function based on differences between observed and model water levels through changing of roughness coefficient, water depth and boundary conditions.…”

Section: Simulationmentioning

confidence: 99%

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Kumar

Leonardi

2022

Preprint

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As climate-driven risks for the world's coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models.Our results highlight the potential of deep learning and statistical models for coastal applications.

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Section: Simulationmentioning

confidence: 99%

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Kumar

Leonardi

2022

Preprint

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“…DA enables automated calibration of models, as well as enhancing forecast capabilities of biogeochemical and hydrological models and improving ecosystem state assessments [ 138 ]. Among the most common DA methods, the Ensemble Steady State Kalman Filter (EnSSKF) [ 139 ] and the Ensemble Kalman Filter (EnKF) [ 140 ] have been widely and successfully used to improve the prediction of lake temperature [ 122 ], suspended particulate matter concentrations [ 141 ], water levels and currents [ 142 ], and algae and algal bloom dynamics [ 143 , 144 ] through the integration of remotely sensed and in situ data. Data integration techniques are variable and site specific depending at the phenomena at hand scale, this is beyond the scope of this paper and thus not addressed.…”

Section: Data Integrationmentioning

confidence: 99%

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

et al. 2021

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Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

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“…Increasing spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques are becoming more favourable and acceptable by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydroinformatics applications ranging from simple data mining for pattern discovery to data driven models and numerical model error correction (Babovic et al, 2001Sannasiraj et al, 2005;Sun et al, 2010;Rao and Babovic, 2010;Karri et al, 2013Karri et al, , 2014Wang and Babovic, 2014). The objectives of this paper is to explore the feasibility of applying average mutual information (AMI) theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e.…”

Section: Introductionmentioning

confidence: 98%

Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

Kurniawan

Ooi

Babovic

2014

Computers & Geosciences

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Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters

Cited by 24 publications

References 27 publications

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Coastal forecast through coupling of Deep Learning and hydro-morphodynamical modelling

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

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