Use of data-driven model to analyse the occurrence patterns of an indicator fish species in river: A case study for Alburnoides eichwaldii (De Filippi, 1863) in Shafaroud River, north of Iran

Zarkami, Rahmat; Darizin, Zeinab; Pasvisheh, Roghayeh Sadeghi; Bani, Aïda; Ghane, Ahmad

doi:10.1016/j.ecoleng.2019.04.018

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…First, implementation of the GA is carried out following the 3 key steps that are motivated by the principles of natural selection and genetics namely selection, crossover and mutation [46]- [49]. The algorithm begins by randomly initiating a population of candidate solutions.…”

Section: ) Implementation Detailsmentioning

confidence: 99%

“…In this work, the crossover was set to a probability of 0.6 while a probability of 0.2 was specified for the mutation. The initial population of candidate solutions and maximum number of generations were both set to 100 [48], [49]. Implementation of the algorithm was carried out in Python using a scikit-learn module namely sklearn-genetic [62] which selects the feature subset that maximizes an objective function.…”

Section: ) Implementation Detailsmentioning

confidence: 99%

“…GAs operate using a wrapper-based approach where an optimal subset of features is selected by analysing the relationship between the entire feature sets and classification models for increased classification performance. GAs have been applied for feature selection and reduction, providing very promising results in a number of applications including vowel [46], learning [47], and cancer data classification [48], fish species prediction [49], biometrics authentication [50], activity recognition [51], spam email detection [52], and emotion and genre detection [53].…”

Section: Introductionmentioning

confidence: 99%

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A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Fabiyi,

Murray,

Zabalza

et al. 2024

IEEE Trans. Agri. Elect.

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Hyperspectral imaging (HSI) has been reported to produce promising results in the classification of rice seeds. However, HSI data often requires the use of dimensionality reduction techniques for the removal of redundant data. Folded Linear Discriminant Analysis (F-LDA) is an extension of Linear Discriminant Analysis (LDA, a commonly used technique for dimensionality reduction) and was recently proposed to address the limitations of LDA, particularly its poor performance when dealing with a small number of training samples which is a usual scenario in HSI applications. This paper presents an improved version of F-LDA, exploring the feasibility of hybridizing a Genetic Algorithm (GA) and F-LDA for effective dimensionality reduction in HSI-based rice seeds classification. The proposed approach, inspired by the previous combination of GA with Principal Component Analysis (PCA), is evaluated on rice seed datasets containing 256 spectral bands. Experimental results show that, in addition to attaining promising classification accuracies of up 96.21%, this novel combination of GA and F-LDA (GA+F-LDA) can further reduce the computational complexity and memory requirement in the standalone F-LDA. It is worth noting that these benefits are not without a slight reduction in classification accuracy when evaluated against those reported for the standard F-LDA (up to 96.99%).

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Section: ) Implementation Detailsmentioning

confidence: 99%

Section: ) Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Fabiyi,

Murray,

Zabalza

et al. 2024

IEEE Trans. Agri. Elect.

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“…Iconic fish species ( sensu Everard & Kataria, 2011) are frequently referred to as ‘focal species’ (e.g. ‘flagship’, ‘umbrella’, and ‘indicator’ species), reflecting the health of the aquatic habitats that support them and their associated biodiversity (Belmar et al, 2018; Zarkami et al, 2019). However, such assertions have often lacked supporting evidence demonstrating clear direct or indirect benefits to society.…”

Section: Introductionmentioning

confidence: 99%

Assessing the societal benefits of mahseer (Tor spp.) fishes to strengthen the basis for their conservation

Everard

Pinder

Claussen³

et al. 2021

Aquatic Conservation

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Mahseer (Tor spp., Cyprinidae) are iconic, large‐bodied, migratory freshwater fishes of Asian rivers. Long valued as a food source and serving other societal roles, these fishes are severely threatened by a broad range of interacting and escalating human impacts, including dam construction, water pollution, over‐exploitation, and habitat destruction. Mahseer fishes are therefore beneficial to people, both directly and as indicators of ecosystem functionality and the associated ecosystem service benefits provided by the rivers that they inhabit. A literature review demonstrated that wild mahseer populations have a direct role in 11 of the 36 ecosystem services assessed and are indicative of a further 18 ecosystem services provided by the habitats that support them. Recognition of this range of ecosystem service benefits is helpful in describing to non‐specialist public and policy‐making communities the diversity of values provided by mahseer fishes and their supporting habitats, and hence the importance of their conservation. Assessment of the broad range of ecosystem services to which mahseer contribute is an approach that could usefully be applied to promote the benefits of conserving other ‘umbrella’ taxa and the habitats that support them.

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Modelling the habitat preferences of the swan mussel (Anodonta cygnea) using data-driven model

Zarkami

Kia

Pasvisheh

2020

Environ Monit Assess

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Use of data-driven model to analyse the occurrence patterns of an indicator fish species in river: A case study for Alburnoides eichwaldii (De Filippi, 1863) in Shafaroud River, north of Iran

Cited by 8 publications

References 33 publications

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Assessing the societal benefits of mahseer (Tor spp.) fishes to strengthen the basis for their conservation

Modelling the habitat preferences of the swan mussel (Anodonta cygnea) using data-driven model

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