Artificial Neural Networks (A New Statistical Approach) Method in Length-Weight Relationships of Alburnus Mossulensis in Murat River (Palu-Elaziğ) Turkey

Özcan, Ebru İfakat

doi:10.15666/aeer/1705_1025310266

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…It has been observed that there is a high affinity between the measured and predicted data and the values obtained with ANN are closer to the real values (Ozcan and Serdar, 2018). Also Ozcan (2019) pointed out that ANN can be used as an alternative method for estimation of population. In the present study, SVR performance metric results are 0.996 and 0.992 values both for the lengthweight of male and female individuals, with 0.996 and 0.995 values and for the length-meat yield of males and females are closer to the values of measurement.…”

Section: Discussionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Gültepe,

Berber,

Gültepe

2024

Preprint

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In this study, predictions of length-weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters obtained from 1416 crayfish in different years to determine the length-weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. No previous studies have been conducted on such population parameters. The analysis results show that most of the models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length-weight relationships and length-meat yields were evaluated for both male and female individuals. Support Vector Regression (SVR) has achieved the best prediction performance accuracy with 0.996 and 0.992 values for the length-weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.

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Section: Discussionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Gültepe,

Berber,

Gültepe

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been observed that there is a high affinity between the measured and predicted data and the values obtained with ANN are closer to the real values [39]. Also Ozcan [40] pointed out that ANN can be used as an alternative method for estimation of population. In the present study, SVR performance metric results are 0.996 and 0.992 values both for the length-weight of male and female individuals, with 0.996 and 0.995 values and for the length-meat yield of males and females are closer to the values of measurement.…”

Section: Discussionmentioning

confidence: 99%

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

GÜLTEPE

Berber

Gültepe

2023

Preprint

View full text Add to dashboard Cite

In this study, predictions of length-weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters obtained from 1416 crayfish in different years to determine the length-weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. No previous studies have been conducted on such population parameters. The analysis results show that most of the models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length-weight relationships and length-meat yields were evaluated for both male and female individuals. Support Vector Regression (SVR) has achieved the best prediction performance with 0.996 and 0.992 values for the length-weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.

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“…There have been numerous studies on predictive modeling using neural networks for the growth of aquatic organisms, specifically focusing on fish (Benzer and Benzer 2016, Benzer and Benzer 2017, Özcan and Serdar 2018, Özcan and Serdar 2019, Özcan 2019, Benzer and Benzer 2020, Benzer and Benzer 2023b, Akkan et al 2024) and crayfish (Türeli Bilen et al 2011, Benzer et al 2015, Benzer and Benzer 2018, Benzer and Benzer 2022.…”

Section: Introductionmentioning

confidence: 99%

The Growth of Crayfish, which Serves as an Indicator of Clean and Healthy Water Ecosystems in the Mediterranean Region

Benzer,

Benzer

2024

AJS

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The narrow-fingered freshwater crayfish, also known as Pontastacus leptodactylus in scientific terms and referred to as such in Turkish, is a prevalent species in the inland waters of the Mediterranean region. This crayfish serves as an indicator of clean and healthy water ecosystems due to its high sensitivity to pollution and environmental changes. Its presence signals the maintenance of water quality and a balanced ecosystem. In a study on 283 freshwater crayfish in Hirfanlı Dam Lake during July and August 2023, it was observed that 53.36% were male and 46.64% were female. Length ranged from 80.44 mm to 121.11 mm, and weight varied between 11.61 g and 43.93 g. Average length and weight for males were 95.9874 ± 6.4603 mm and 24.1861 ± 5.3696 g, for females were 95.0981 ± 5.5519 mm and 21.5193 ± 3.4488 g, and for combined sexes were 95.5724 ± 6.0594 mm and 22.9422 ± 4.7579 g. The length-weight relationship (LWR) was established for females (W=0.00166359 ×TL2.0767), males (W=0.00019587×TL2.5651), and all individuals (W=0.00037111 ×TL2.4162). Exponential values "b" for LWR were 2.0767 (r2=0.993), 2.5651 (r2=0.986), and 2.4162 (r2=0.987), respectively. The study compared traditional LWR approaches with artificial intelligence methods for growth analysis, suggesting the latter as a viable alternative in assessing freshwater crayfish growth in aquatic systems.

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Artificial Neural Networks (A New Statistical Approach) Method in Length-Weight Relationships of Alburnus Mossulensis in Murat River (Palu-Elaziğ) Turkey

Cited by 6 publications

References 14 publications

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

The Growth of Crayfish, which Serves as an Indicator of Clean and Healthy Water Ecosystems in the Mediterranean Region

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