Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

Hammoud, Mohamad Abed El Rahman; Mittal, H. V. R.; Maître, Olivier Le; Hoteit, Ibrahim; Knio, Omar M.

doi:10.3389/fmars.2023.1185106

Cited by 3 publications

(1 citation statement)

References 95 publications

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“…where c 0 = 0.310, c 1 = −2.621, c 2 = 2.910, c 3 = −3.238 and c 4 = 1.036, which were obtained following a LASSO regularized regression approach [63] using the same dataset adopted to train the BNN.…”

Section: Oc4mentioning

confidence: 99%

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

Hammoud,

Papagiannopoulos,

Brewin

et al. 2024

Preprint

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This study explores the use of Bayesian Neural Networks (BNNs) for estimating chlorophyll-a concentration ([CHL-a]) from remotely sensed data. The BNN model enables uncertainty quantification, offering additional layers of information compared to traditional ocean colour models. An extensive in situ bio-optical dataset is utilized, generated by merging 27 data sources across the world’s oceans. The BNN model demonstrates remarkable capability in capturing mesoscale features and ocean circulation patterns, providing comprehensive insights into spatial and temporal variations in [CHL-a] across diverse marine ecosystems. In comparison to established ocean colour algorithms, such as Ocean Colour 4 (OC4), the BNN shows comparable performance in terms of correlation coefficients, errors, and biases when compared with the in situ data. The BNN, however, further provides critical information about the distribution of [CHL-a], which can be used to assess uncertainties in the prediction. Moreover, the BNN model’s ability to provide more information for coastal waters, especially with higher spatial resolution imagery, offers valuable advantages for marine research and management. By quantifying uncertainty, the model builds confidence in [CHL-a] predictions, even in regions with limited data coverage. This is reflected by the spread in the model predictions, where the BNN can detect a range of uncertainty bounds that reflect confidence in the retrievals. Introducing BNNs as probabilistic ocean colour models represents a significant advancement, enabling more accurate and reliable predictions of [CHL-a] in the global oceans.

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Section: Oc4mentioning

confidence: 99%

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

Hammoud,

Papagiannopoulos,

Brewin

et al. 2024

Preprint

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show abstract

Iterative data-driven construction of surrogates for an efficient Bayesian identification of oil spill source parameters from image contours

El Mohtar,

Le Maître,

Knio

et al. 2024

Comput Geosci

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Oil spill risk analysis for the NEOM shoreline

Mittal,

Hammoud,

Carrasco

et al. 2024

Sci Rep

Self Cite

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A risk analysis is conducted considering an array of release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high marine traffic. The evolution of oil spills released by these sources is simulated using the MOHID model, driven by validated, high-resolution met-ocean fields of the Red Sea. For each source, simulations are conducted over a 4-week period, starting from first, tenth and twentieth days of each month, covering five consecutive years. A total of 180 simulations are thus conducted for each source location, adequately reflecting the variability of met-ocean conditions in the region. The risk associated with each source is described in terms of amount of oil beached, and by the time required for the spilled oil to reach the NEOM coast, extending from the Gulf of Aqaba in the North to Duba in the South. To further characterize the impact of individual sources, a finer analysis is performed by segmenting the NEOM shoreline, based on important coastal development and installation sites. For each subregion, source and release event considered, a histogram of the amount of volume beached is generated, also classifying individual events in terms of the corresponding arrival times. In addition, for each subregion considered, an inverse analysis is conducted to identify regions of dependence of the cumulative risk, estimated using the collection of all sources and events considered. The transport of oil around the NEOM shorelines is promoted by chaotic circulations and northwest winds in summer, and a dominant cyclonic eddy in winter. Hence, spills originating from release sources located close to the NEOM shorelines are characterized by large monthly variations in arrival times, ranging from less than a week to more than 2 weeks. Similarly, large variations in the volume fraction of beached oil, ranging from less then 50% to more than 80% are reported. The results of this study provide key information regarding the location of dominant oil spill risk sources, the severity of the potential release events, as well as the time frames within which mitigation actions may need to deployed.

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Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

Cited by 3 publications

References 95 publications

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

Iterative data-driven construction of surrogates for an efficient Bayesian identification of oil spill source parameters from image contours

Oil spill risk analysis for the NEOM shoreline

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