Seasonal distribution patterns of Scomberomorus commerson in the Taiwan Strait in relation to oceanographic conditions: An ensemble modeling approach

Mondal, Sandipan; Lee, Ming-An; Weng, Jinn-Shing; Osuka, Kennedy Edeye; Chen, Yu Kai; Ray, Aratrika

doi:10.1016/j.marpolbul.2023.115733

Cited by 4 publications

(2 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These characteristics constitute an asset not only for the case study, but also for addressing problems common to most European and Mediterranean industrial and logistics areas, where the proposed model would retain its effectiveness. This is easily linked to the UN Sustainable Development Goals (8.4, 8.8; 9.1, 9.4; 11.5, 11.6, 11.7, 11.a; 13.1; 15.5, 15.8, 15.9, 15.a), which transpose the proposed approach into a global perspective [57,58].…”

Section: Discussionmentioning

confidence: 99%

Eco-Zip: Climate-Proofing an International Logistic Hub

Bertin,

Vincenti

2024

Sustainability

View full text Add to dashboard Cite

One of the most significant challenges in urban design today is the adaptation of industrial and logistical areas to climate change. These vast hubs are sites with a high degree of vulnerability concerning management of stormwater and heat waves. However, local planning struggles to develop appropriate tools for these essential structures. The lack of tools disregards climate influence on the future economy and the quality and safety of workplaces. Moreover, intervening in such large areas can increase green and blue systems. The research described in this article reflects on their possible evolution in a climate change mitigation and adaptation scenario from a global perspective, helping to achieve UN Sustainable Development Goals (8; 9; 11; 13; 15). The study intends to propose the redefinition of the settlement scheme as an adaptation tool. This strategy takes the Industrial Zone of Padova as a test site, and it is developed to reduce risks related to extreme climate events by favouring the action of green infrastructures and, at the same time, exploiting the social and economic opportunities that may emerge from this urban transformation.

show abstract

Section: Discussionmentioning

confidence: 99%

Eco-Zip: Climate-Proofing an International Logistic Hub

Bertin,

Vincenti

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…The use of statistical regression models, artificial intelligence models, and classification and regression tree models has been greatly augmented due to developments in technology [26]. The latest models offer enhanced adaptability, precise forecasting, and the ability to handle extensive datasets, outperforming prior methods in terms of effectiveness [27]. In recent years, ensemble methods such as core bagging, boosting, and stacking have garnered significant attention due to their enhanced performance compared to single-algorithm models.…”

Section: Introductionmentioning

confidence: 99%

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

Teng,

Mondal,

et al. 2024

JMSE

Self Cite

View full text Add to dashboard Cite

The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

show abstract