Towards a classification of vulnerability of small-scale fisheries

Islam, Mohammad Mahmudul; Chuenpagdee, Ratana

doi:10.1016/j.envsci.2022.03.023

Cited by 18 publications

(6 citation statements)

References 74 publications

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“…Challenges in the existing literature are rooted in the lack of a comprehensive understanding of fish landing classification complexities [3]. These complexities are crucial for developing effective resource management strategies.…”

Section: *Author For Correspondencementioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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Despite the numerous fish classification systems developed over the years, they often suffer from poor prediction accuracy, necessitating further improvement. This study addresses this issue by comparing the performance of different classifiers on fish landing datasets (2005-2019) obtained from the Department of Fisheries Malaysia (DOFM). The focus is on the East Coast of Peninsular Malaysia. The classifiers evaluated include Sequential minimal optimization (SMO), naïve Bayes (NB), multi-layer perception (MLP), instance-based for k-nearest neighbor (IBK), and random forest (RF). The performance of each classifier is assessed using classification accuracy and confusion matrix metrics, employing a 10-fold cross-validation method. Additionally, a multi-classification technique is applied to enhance the accuracy of individual classifiers and determine the most effective approach for generating an accurate dataset. The study reveals that the combinations RF+SMO+NB+MLP and SMO+RF+NB+MLP outperform single classifiers and other fusion methods, achieving the highest accuracy at 80.95%. This indicates that a multi-classifier approach can significantly enhance the performance of individual classifiers. The findings highlight the effectiveness of the multi-classifier approach in improving prediction accuracy for fish classification. The identified combinations, RF+SMO+NB+MLP and SMO+RF+NB+MLP, demonstrate superior performance and can serve as a robust methodology for fish landing classification in the context of the East Coast of Peninsular Malaysia. Further research and implementation of such multi-classifier approaches could contribute to more accurate and reliable fish classification systems.

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Section: *Author For Correspondencementioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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“…Someone or something (e.g., small-scale fisheries are more vulnerable than large-scale fisheries) may be considered vulnerable [6,67]. SSFs are at risk because of a wide range of complex environmental, economic, and political stressors and changes [68]. SSF communities are particularly sensitive to local and global change processes because of their heavy reliance on natural resources and close linkages to the coastal environment [2].…”

Section: Key Vulnerabilities From Water-quality Issuesmentioning

confidence: 99%

Exploring Water Quality as a Determinant of Small-Scale Fisheries Vulnerability

Nair,

Nayak

2023

Sustainability

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Water quality is a fundamental indicator of coastal ecosystem health. Maintaining appropriate levels of water quality is critical for the growth of aquatic species and the livelihoods of dependent small-scale fishery (SSF) communities. However, natural (e.g., cyclones, floods) and hu-man-induced (e.g., hydrological changes, varied fishing techniques) factors create cumulative stress on these systems, leading to environmental and socioeconomic challenges. This often manifests as food insecurity, occupational displacement, and biodiversity loss. Despite existing research on coastal sustainability and resilience, the intricate connection between water-quality variations and social–ecological vulnerabilities remains understudied. This paper addresses this gap, focusing on the interplay between water quality changes and the vulnerabilities faced by SSF communities. Using the Chilika Lagoon in India as a case study, this synthesis paper examines water-quality processes and their impact on community vulnerabilities over three decades. It analyses various coping and adaptive responses of the fisher communities and the potential of their actions for creating viable small-scale fisheries. Our findings suggest ways in which SSF communities can respond to these vulnerabilities and help foster knowledge for their transition to viability.

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“…Many factors affect the effectiveness of vulnerability classification, among which the main elements are dimensionality reduction processing techniques [16] and classification algorithms [17]. The purpose of dimensionality reduction processing techniques is to convert a high-dimensional vulnerability dataset into a low-dimensional representation while trying to preserve the structure and information of the vulnerability data, that is, how to represent a vulnerability with as few feature words as possible; classification algorithms are the process of automatically classifying unknown vulnerability information based on known vulnerability training datasets.…”

Section: Related Workmentioning

confidence: 99%

IoV Vulnerability Classification Algorithm Based on Knowledge Graph

Wang,

Song

et al. 2023

Electronics

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With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to address the low occurrence frequency and incomplete information associated with IoV vulnerabilities, resulting in decreased precision and recall rates of classifiers. To address these challenges, an effective vulnerability classification algorithm (KG-KNN), is proposed, designed to handle imbalanced sample data. KG-KNN integrates the vulnerability information of IoV and the association relationship between features by constructing a feature knowledge graph to form a complete knowledge system. It adds the correlation relationship between features to the similarity calculation, calculates vulnerability similarity from multiple dimensions, and improves the prediction performance of the classifier. The experimental results show that compared to the k-NearestNeighbor (KNN), Support Vector Machine (SVM), Deep Nueral Network (DNN) and TFI-DNN classification algorithms, KG-KNN can effectively deal with imbalanced sample data and has different degrees of improvement in precision, recall, and the F1 score.

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Towards a classification of vulnerability of small-scale fisheries

Cited by 18 publications

References 74 publications

Multi-classifier models to improve the accuracy of fish landing application

Multi-classifier models to improve the accuracy of fish landing application

Exploring Water Quality as a Determinant of Small-Scale Fisheries Vulnerability

IoV Vulnerability Classification Algorithm Based on Knowledge Graph

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