Ensemble modeling of black pomfret (<i>Parastromateus niger</i>) habitat in the Taiwan Strait based on oceanographic variables

Mondal, Sandipan; Lee, Ming-An; Wang, Yi-Chen

doi:10.7717/peerj.14990

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…This dataset has undergone rigorous validation against in situ observations and other sea surface temperature (SST) and salinity products, demonstrating robust consistency with independent data sources (e.g., Guinehut et al, 2012;Jean-Michel et al, 2021;Gasparin et al, 2023). Its applicability spans oceanographic and climate research domains (e.g., Xie et al, 2016;Cai et al, 2021;Karnauskas, 2022;Mondal et al, 2023). Accessible from the Copernicus Marine Environment Monitoring Services CMEMS data archive, the dataset covers the temporal span from 1993 to the present.…”

Section: Data Sourcementioning

confidence: 84%

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

Ayinde,

YU,

2023

Preprint

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The rising sea level due to climate change poses a critical threat, particularly affecting vulnerable low-lying coastal areas such as the Gulf of Guinea (GoG). This impact necessitates precise sea level prediction models to guide planning and mitigation efforts for safeguarding coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020. The assessment covers three distinct periods (1993–2002, 2003–2012, and 2013–2020) and investigates connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Additionally, the performance of artificial neural networks (LSTM and MLPR) and machine learning techniques (MLR, GBM, and RFR) is evaluated to optimize sea level predictions. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the north, with a total linear trend of 88 mm/year over the entire period. The highest decadal trend (38.7 mm/year) emerged during 2013–2020, and the most substantial percentage increment (100%) occurred in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. Model evaluation indicates RFR and GBR as accurate methods, reproducing interannual sea level patterns with 97% and 96% accuracy, respectively. These findings contribute essential insights for effective coastal management and climate adaptation strategies in the GoG.

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Section: Data Sourcementioning

confidence: 84%

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

Ayinde,

YU,

2023

Preprint

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show abstract

“…This dataset has undergone rigorous validation against in situ observations and other sea surface temperature (SST) and salinity products, demonstrating robust consistency with independent data sources (e.g., 41 – 43 ). Its applicability spans oceanographic and climate research domains (e.g., 44 – 47 ). Accessible from the Copernicus Marine Environment Monitoring Services CMEMS data archive, the dataset covers the temporal span from 1993 to the present.…”

Section: Methodsmentioning

confidence: 99%

Sea level variability and modeling in the Gulf of Guinea using supervised machine learning

Ayinde,

Yu,

2023

Sci Rep

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The rising sea levels due to climate change are a significant concern, particularly for vulnerable, low-lying coastal regions like the Gulf of Guinea (GoG). To effectively address this issue, it is crucial to gain a comprehensive understanding of historical sea level variability, and the influencing factors, and develop a reliable modeling system for future projections. This knowledge is essential for informed planning and mitigation strategies aimed at protecting coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020, covering three distinct periods (1993–2002, 2003–2012, and 2013–2020). It investigates the connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Furthermore, the study evaluates the performance of supervised machine learning techniques to optimize sea level modeling. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the northern region, with a total linear trend of 88 mm over the entire period. The highest decadal trend (38.7 mm) emerged during 2013–2020, with the most substantial percentage increment (100%) occurring in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. The evaluation of supervised learning modeling methods indicates that Random Forest Regression and Gradient Boosting Machines are the most accurate, reproducing interannual sea level patterns in the GoG with 97% and 96% accuracy. These models could be used to derive regional sea level projections via downscaling of climate models. These findings provide essential insights for effective coastal management and climate adaptation strategies in the GoG.

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“…By using R 4.2.3, we performed GAM analysis to identify the effect of each oceanographic variable on the standardized CPUE for squid. This is because GAMs are valuable in oceanography and fisheries due to their flexibility in modeling complex relationships in data, handling non-linearity, interactions, and irregularly sampled data (Mondal et al, 2023a;Mondal et al, 2023b). Each oceanographic variable was considered as a predictor variable, whereas the standardized CPUE for squid was considered as a response variable.…”

Section: Step V -Impact Of Oceanographic Variables On Standardized Cp...mentioning

confidence: 99%

Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices

Haghi Vayghan,

Ray,

Mondal

et al. 2024

Front. Mar. Sci.

Self Cite

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Understanding the spatial arrangement of species in maritime settings necessitates the study of oceanography. Hence, doing a study on the correlation between oceanography and species dispersion is imperative, considering the impacts of global climate change. The study used a generalized additive modeling approach to analyze the influence of oceanographic conditions on the distribution of swordtip squid in northeastern Taiwan, integrating fishing data, climatic oscillation and oceanography. Among seven oceanographic characteristics, bottom sea temperature (SSTB), sea surface height (SSH), sea surface chlorophyll (SSC), and sea surface temperature (SST) showed significant influence in generalized additive model (GAM) analysis (combined deviance explained: 40.30%). The monthly catch rate of swordtip squid is influenced by six climatic oscillations, with the Pacific Decadal Oscillation having the most significant impact, accounting for 31% of the distribution, followed by the North Pacific Gyre Oscillation at 10.8% and the Western Pacific Oscillation at 6.05%. From 2015 to 2019, the main areas for squid fishing were situated in the northeastern waters of Taiwan, precisely within the geographical coordinates of 25°N to 28°N and 121.5°E to 125°E. This study provides crucial insights for managing swordtip squid fisheries in Taiwan's northwest waters, highlighting the importance of incorporating oceanographic conditions relating to climate change information into decision-making to protect global ocean fisheries and their dependent communities.

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Ensemble modeling of black pomfret (Parastromateus niger) habitat in the Taiwan Strait based on oceanographic variables

Cited by 6 publications

References 48 publications

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

Sea level variability and modeling in the Gulf of Guinea using supervised machine learning

Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices

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