The Importance of Data Reliability and Usability When Assessing Impacts of Marine Mineral Oil Spills

Wait, A. Dallas

doi:10.3390/toxics9110302

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The most critical prerequisites for increasing data usability through clinical information structuring were governance and competency. Wait [37] assisted in developing attainable data quality objectives and insight into obtaining reliable results that adequately support the findings when reviewed by others.…”

Section: Data Usabilitymentioning

confidence: 99%

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Lee

Jeong

Jung

et al. 2022

JPM

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With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.

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Section: Data Usabilitymentioning

confidence: 99%

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Lee

Jeong

Jung

et al. 2022

JPM

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“…Oil spills describe the inadvertent or deliberate introduction or discharge of petroleum hydrocarbon products and their byproducts into any environment [1,2,3,4,5,6]. These environments comprise the air, water, or land.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Oil Spill Impacted Vegetation in the Niger Delta Region of Nigeria: A Google Earth Engine Derived Vegetation Indices Approach

Wekpe,

Idisi

2024

JGEESI

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New and emerging cases of oil spill incidents are reported almost on a weekly basis in the Niger Delta region of Nigeria with accusations and counter-accusations as to the claims made by interested parties on perceived impacts of the spills on the environment and its associated constituents. This study applied the capabilities offered by the Google Earth Engine (GEE) platform to monitor long-term vegetation dynamics as a result of exposure to pollution emanating from crude oil spills in the Niger Delta region of Nigeria. The capabilities offered by GEE provide a platform for rapid access to big data for the assessment of environmental change, especially in the Niger Delta with its difficult terrain and security concerns. This study considered oil spill incidents in vegetated terrestrial locations in the Niger Delta across ten years. Fifteen locations spread across the region with oil spill incidents identified as large incidents being selected (>50 <5000bbl). Results of the statistical analysis performed on the vegetation indices data generated from GEE suggest that the analysis of long-term vegetation indices using GEE can provide a broad view of the impact of oil spills on vegetation over time if the spills are relatively large or the spills are repetitive. However, when the spills were relatively small, there was no statistically significant variation in the spectral signatures of the vegetation over time. This suggests that for large spills, GEE-derived vegetation indices can be a very useful synoptic tool in monitoring oil spill occurrence and impact on vegetated terrestrial environments in the Niger Delta and elsewhere where environmental accessibility is a challenge.

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“…The problem of oil spills has been identified as a potentially serious problem which the people and the environment of the Niger Delta have to continually contend with (UNEP, 2011; Amnesty International 2015; Wekpe, 2018;Ozigis et al, 2019;Akpokodje, 2020;Ugwu et al, 2021). Oil spills describe the inadvertent or deliberate introduction or discharge of petroleum hydrocarbon products and their byproducts into any environment (Guo & Zhnag, 2014;Durango-Cordero et al, 2018;Ndimele et al, 2018;Carpenter, 2019;Ozigis et al, 2019;Wait, 2021). The occurrence of oil spills has generally increased in its frequency in the last decade, due to increased international transportation of cargo driven by rising consumption, energy generation and industrial usage (Berenshtein et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Environmental Sensitivity Index Mapping (TESIM) of the Coastal Areas of the Niger Delta Region of Nigeria

Wekpe

2023

Preprint

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It can be argued that despite years of research, the true impacts of oil spill pollution on the environment and human sources of livelihoods are yet to be completely understood. Tools such as environmental sensitivity index (ESI) mapping can provide useful information that can significantly mitigate or help eliminate the known impacts of crude oil pollution, by proactively identifying environmentally sensitive areas. This study adapted the analytical hierarchy process (AHP) and geographic information systems (GIS) based multiple criteria analysis (MCA) approach in the objective determination of sensitive sections of the NDR coastal areas, by combining several competing and or related evaluation criteria. The AHP was used to determine and assign relative weights to the data layers through a pairwise comparison evaluation matrix of mutually exclusive evaluation criteria using the Delphi technique, while the MCA was used to aggregate the evaluation criteria into a single map. The results of the MCA analysis show that over 57% of the study area is sensitive to oil spill pollution in one form or the other. 68% of the identified sensitive areas are classified as extremely environmentally sensitive with 11,752.14km2, while 7.1% are classified as less sensitive. This relative classification of environmental sensitivity in the study area is termed the Niger Delta Relative Environmental Sensitivity Index (NDRESI). The implication of these results is that more has to be done if the very sensitive environment of the NDR is to be conserved by tougher environmental legislations, environmental monitoring and environmental education.

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The Importance of Data Reliability and Usability When Assessing Impacts of Marine Mineral Oil Spills

Cited by 6 publications

References 29 publications

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Long-Term Monitoring of Oil Spill Impacted Vegetation in the Niger Delta Region of Nigeria: A Google Earth Engine Derived Vegetation Indices Approach

Terrestrial Environmental Sensitivity Index Mapping (TESIM) of the Coastal Areas of the Niger Delta Region of Nigeria

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