Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843

Ozcan, Ebru Ifakat

doi:10.26881/oahs-2024.2.06

Artykuł - szczegóły

Tytuł artykułu

Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843

Autorzy

Ozcan Ebru Ifakat

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.26881/oahs-2024.2.06

Warianty tytułu

Języki publikacji

Abstrakty

In this study, predictions were made on the growth performance of Alburnus sellal Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. Statistical growth models used in fish biology and results obtained from artificial neural networks were compared. A total of 239 samples were used in this comparison. It was determined that the population is distributed across age groups 0–VII. The relationship between length and weight was calculated as W = 0.0046L3.198 for all individuals. The von Bertalanffy growth parameters were calculated for all individuals: Lt = 21.93 [1 – e–0.158 (t + 2.11)]; Wt = 102.29 [1 – e–0.158 (t + 2.11)]3.198. The growth performance index (Ф’) value was 1.880 for all individuals. The condition factor varied between 0.479 and 1.115 for females and between 0.533 and 1.076 for males. The Mean Absolute Percent Error (MAPE) statistic was used, which is a widely used method to measure the accuracy of the predictions made. It was determined that ANNs MAPE (%) values were better than MAPE values calculated for the length–weight relationship and von Bertalanffy growth function models for A. sellal. This study shows that ANNs can be used as an alternative useful method for predicting population parameters. ANN models are therefore an effective tool to describe fish growth parameters. They have been found to be a useful predictive tool. The developed models can be used to predict future sustainable fish management.

Słowa kluczowe

Alburnus sellal artificial neural networks growth parameters MAPE (%) Munzur River

Wydawca

Institute of Oceanography University of Gdańsk

Czasopismo

Oceanological and Hydrobiological Studies

Rocznik

2024

Tom

Vol. 53, No. 2

Strony

153--163

Opis fizyczny

Bibliogr. 61 poz., fot., tab., wykr.

Twórcy

autor

Ozcan Ebru Ifakat

ebruozer@munzur.edu.tr

Munzur University Faculty of Fisheries, Tunceli, Turkiye

Bibliografia

[1]. Akgül, I. (2003). Zaman Serilerinin Analizi ve ARIMA Modelleri. Der Yayınları.
[2]. Alkan Uçkun, A., & Gökçe, D. (2015). Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey). Turkish Journal of Zoology, 39, 1-14. https://doi.org/10.3906/zoo-1211-13
[3]. Başusta, N., & Çicek, E. (2006). Length-weight relationships for some teleost fishes caught in Ataturk Dam Lake on southeastern Anatolia, Turkey. -. Journal of Applied Ichthyology, 22, 279-280. https://doi.org/10.1111/j.1439-0426.2006.00778.x
[4]. Beamish, R. J., & Fournier, D. A. (1981). A method for comparing the precision of a set of age determinations. Canadian Journal of Fisheries and Aquatic Sciences, 38(8), 982-983. https://doi.org/10.1139/f81-132
[5]. Bektaş, Y., Aksu, I., Kaya, C., Bayçelebi, E., Küçük, F., & Turan, D. (2020). Molecular systematics and phylogeography of the genus Alburnus Rafinesque, 1820 (Teleostei, Leuciscidae) in Turkey. Mitochondrial DNA. Part A, DNA Mapping, Sequencing, and Analysis, 31, 273-284. Advance online publication. https://doi.org/10.1080/24701394.2020.1791840
[6]. Bellido, J. M., Pierce, G. J., Romero, J. L., & Millan, M. (2000). Use of frequency analysis methods to estimate growth of anchovy (Engraulis encrasicolus L. 1758) in the Gulf of Cadiz (SW Spain). Fisheries Research, 48(2), 107-115. https://doi.org/10.1016/S0165-7836(00)00183-1
[7]. Benzer, S., Karasu Benli, C., & Benzer, R. (2015). The comparison of growth with length-weight relation and artificial neural networks of crayfish, Astacus leptodactylus, in Mogan Lake. Journal of the Black Sea/Mediterranean Environment, 21(2), 208-223.
[8]. Benzer, S., & Benzer, R. (2016). Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Applied Ecology and Environmental Research, 14(2), 543-554. https://doi.org/10.15666/aeer/1402_543554
[9]. Benzer, S., Benzer, R., & Gunal, A. C. (2017). Artificial neural networks approach in length-weight relation of crayfish (Astacus leptodactylus Eschscholtz, 1823) in Eğirdir Lake, Isparta, Turkey. Journal of Coastal Life Medicine, 5(8), 330-335. https://doi.org/10.12980/jclm.5.2017J7-19
[10]. Benzer, S., & Benzer, R. (2018). New Perspectives for Predicting Growth Properties of Crayfish (Astacus leptodactylus Eschscholtz, 1823) in Uluabat Lake. Pakistan Journal of Zoology, 50(1), 35-45.
[11]. Brosse, S., Guegan, J., Tourenq, J., & Lek, S. (1999). The Use of Artificial Neural Networks to Assess Fish Abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling, 120(2-3), 299-311. https://doi.org/10.1016/S0304-3800(99)00110-6
[12]. Bulut, H., Sesli, A., & Tepe, R. (2021). Uzunçayır Baraj Gölü Güncel Zooplanktonunun Bazı Su Kalite Parametreleri ile Değerlendirilmesi. International Journal of Pure and Applied Sciences, 7(3), 429-441. https://doi.org/10.29132/ijpas.938647
[13]. Bulut, H. (2023) Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye. Oceanological and Hydrobiological Studies, 52(4), 502-515. https://doi.org/10.26881/oahs-2023.4.11
[14]. Çiçek, T. (2013). Dicle Nehri’nde yaşayan Carassius gibelio, Acanthobrama marmid ve Alburnus mossulensis türlerinin biyolojisi üzerine araştırmalar. (392575). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp
[15]. Çiçek, E., Sungur, S., & Fricke, R. (2020). Freshwater lampreys and fishes of Turkey; a revised and updated annotated checklist 2020. Zootaxa, 4809(2), 2. https://doi.org/10.11646/zootaxa.4809.2.2 PMID:33055935
[16]. Çuhadar, M., Güngör, İ. & Göksu, A. (2009).Turizm Talebinin Yapay Sinir Ağları İle Tahmini ve Zaman Serisi Yöntemleri ile Karşılaştırmalı Analizi: Antalya İline Yönelik Bir Uygulama. Süleyman Demirel Üniversitesi iktisadi ve idari Bilimler Fakültesi Dergisi, 14(1): 99-114.
[17]. Duman, Ö. V., & Başusta, N. (2013). Age and growth characteristics of marbled electric ray Torpedo marmorata (Risso, 1810) inhabiting Iskenderun Bay, North-eastern Mediterranean Sea. Turkish Journal of Fisheries and Aquatic Sciences, 13(3), 551-559. http://dx.doi.org/10.4194/1303-2712-v13_3_19
[18]. Düşükcan, M., Eroğlu, M., & Çoban, M. Z. (2022). Özlüce Baraj Gölü (Elazığ-Bingöl, Türkiye)’nde yaşayan Alburnus sellal Heckel, 1843 populasyonunda bazı morfometrik ve meristik özelliklerinin incelenmesi. Acta Aquatica Turcica, 18(4), 486-494. https://doi.org/10.22392/actaquatr.1117235
[19]. Ekici, B. B., & Aksoy, U. T. (2009). Prediction of building energy consumption by using artificial neural networks. -. Advances in Engineering Software, 40, 356-362. https://doi.org/10.1016/j.advengsoft.2008.05.003
[20]. Emeksiz, C., Doğan, Z., Gökrem, L., & Yavuz, A. H. (2016). “Tokat bölgesi rüzgar karakteristiğinin istatistiksel yöntemler ile incelenmesi”, Politeknik Dergisi, 19( 4), 481-489.
[21]. Erkoyuncu, İ. (1995). Balıkçılık Biyolojisi ve Populasyon Dinamigi. Ondokuz Mayıs Üniversitesi Yayınları, Sinop.
[22]. Fazlı, H., Daryanabard, G. R., Janbaz, A., Naderi, M., Mirzaei, R., Hosseinpour, H., & Vahedi, N. (2019). Age and growth of Alburnus mossulensis Heckel, 1843 in Azad Dam Reservoir and Komasi River in Kordestan Province, Iran. Iranian Journal of Fisheries Science, 18(4), 672-683. https://doi.org/10.22092/ijfs.2018.117745
[23]. Freyhof, J., Kaya, C., & Ali, A. (2021). A Critical Checklist of the Inland Fishes Native to the Euphrates and Tigris Drainages. In L. A. Jawad (Ed.), Tigris and Euphrates Rivers: Their Environment from Headwaters to Mouth. Aquatic Ecology Series (Vol. 11). Springer., https://doi.org/10.1007/978-3-030-57570-0_35
[24]. Gayanilo, F. C., & Pauly, D. (1997). FAO-ICLARM Stock Assessment Tools (FISAT)”, Reference Manual. [Rome, Italy.]. FAO Computerized Information Series (Fisheries), (8), 1997.
[25]. Hedayati, S. S., Ghafari Farsani, H., & Gerami, M. H. (2016). Length-weight relationships of two fish species from Gamsiab Reservoir, western Iran: Alburnus mossulensis Heckel, 1843 and Luciobarbus esocinus Heckel, 1843. Journal of Applied Ichthyology, 32, 139-140. https://doi.org/10.1111/jai.12915
[26]. Jouladeh-Roudbar, A., Ghanavi, H. R., & Doadrio, I. (2020). Ichthyofauna from Iranian freshwater: Annotated checklist, diagnosis, taxonomy, distribution and conservation assessment. Zoological Studies (Taipei, Taiwan), 59(21), e21. Advance online publication. https://doi.org/10.6620/ZS.2020.59-21 PMID:33456548
[27]. Keivany, Y., Ghorbani, M., & Paykan-Heyrati, F. (2017). Age and growth of Alburnus mossulensis (Cyprinidae) in Bibi-Sayyedan River of Isfahan Province. Iranian Journal of Fisheries Science, 16(4), 1164-1177.
[28]. Keskin, S. B. (2016). Aşağı Fırat Havzası Alburnus mossulensis (Heckel, 1843) popülasyonu için bazı popülasyon dinamiği parametrelerinin belirlenmesi. (447113). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp
[29]. Krenker, A., Bešter, J. & Kos, A. (2011). Introduction to the Artificial Neural Networks. - Artificial Neural Networks-Methodological Advances and Biomedical Applications. https://doi.org/10.5772/15751.
[30]. Lagler, K. F., Bardach, J. E., Miller, R. R., & Passino, D. R. M. (1977). Ichtyology. John Wiley and Sons.
[31]. Le Cren, E. D. (1951). The length-weight relationships and seasonal cycle in gonad weight and condition in Perch (Perca fluviatilis). Journal of Animal Ecology, 20, 201-219. https://doi.org/10.2307/1540
[32]. Lewis, C. D. (1982). Industrial and business forecasting methods. Butterworths.
[33]. Joy, K. M., & Death, R. G. (2004). Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural Networks. Freshwater Biology, 49, 1036-1052. https://doi.org/10.1111/j.1365-2427.2004.01248.x
[34]. Maravelias, C. D., Haralabous, J., & Papaconstantinou, C. (2003). Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine Ecology Progress Series, 255, 249-258. https://doi.org/10.3354/meps255249
[35]. Mohamed, A. M., Aufy, L. A., & Jasim, B. M. (2015). Some Biological Aspects of the Bleak, Alburnus mossulensis in the Southern Reaches of Euphrates River, Iraq. Asian Journal of Applied Sciences, 3(2), 277-285.
[36]. Mohammadian-Kalat, T., Esmaeili, H. R., Aliabadian, M., & Freyhof, J. (2017). Re-description of Alburnus doriae, with comments on the taxonomic status of A. amirkabiri, A. mossulensis, A. sellal and Petroleuciscus esfahani (Teleostei: Cyprinidae). Zootaxa, 4323(4), 487-502. https://doi.org/10.11646/zootaxa.4323.4.3
[37]. Nikolsky, G. V. (1963). The Ecology of Fishes (Trans.From The Russian by L.Birkett). Academic Press.
[38]. Ozcan, E.I. & Serdar, O. (2018). Artifical neural networks as new alternative method to estimating some population parameters of tigris loach (Oxynoemacheilus tigris (Heckel, 1843)) in the Karasu River, Turkey. Fresenius Environmental Bulletin, 27(12B/2018), 9840-9850.
[39]. Ozcan, E. I., & Serdar, O. (2019). Evaluation of a new computer method (ANNs) and traditional methods (LWRs and VBGF) in the calculation of some growth parameters of two cyprinid species. Fresenius Environmental Bulletin, 28(10), 7644-7654.
[40]. Ozcan, E. I. (2019). Artificial neural networks (a new statistical approach) method in length-weight relationships of Alburnus mossulensis in Murat River (Palu-Elazığ) Turkey. Applied Ecology and Environmental Research, 17(5), 10253-10266. https://doi.org/10.15666/aeer/1705_1025310266
[41]. Özcan, E. İ., & Serdar, O. (2021). Pülümür Nehri (Tunceli)‘ndeki Alburnus mossulensis (Mossul bleak) Heckel, 1843 ’ün Boy-Ağırlık İlişkisi ve Kondisyon Faktörü. Journal of Anatolian Environmental and Animal Sciences, 6(4), 614-620. https://doi.org/10.35229/jaes.976636
[42]. Öztemel, E. (2012). Yapay Sinir Ağları. Papatya Yayıncılık Eğitim Bilgisayar Sistem Sanayi ve Ticaret A.Ş. 232 s. ISBN: 978-975-6797-39-6.
[43]. Radkhah, A. (2016). A study on length-weight relationship and condition factor of Alburnus mossulensis in Hamzeh-Ali Region from Chaharmahal and Bakhtiari Province, Iran. International Journal of Fisheries and Aquatic Studies, 4(1), 124-125.
[44]. Saleem, W., Zain-ul-abdein, M., Ijaz, H., Mahfouz, A. S. B., Ahmed, A., Asad, M., & Mabrouki, T. (2017). Computational Analysis and Artificial Neural Network Optimization of Dry Turning Parameters AA2024-T351. -. Applied Sciences (Basel, Switzerland), 7, 642. https://doi.org/10.3390/app7060642
[45]. Saler, S., & Haykır, H. (2011). Zooplankton composition of Pulumur Stream (Tunceli-Turkey). Journal of Animal and Veterinary Advances, 10(11), 1401-1403. https://doi.org/10.3923/javaa.2011.1401.1403
[46]. Saler, S., Haykır, H. & Baysal, N. (2014). Zooplankton of Uzunçayır Dam Lake (Tunceli-Turkey). Journal of FisheriesSciences. com, 8(1), 1-7. (ShortCommunication). https://doi.org/10.3153/jfscom.2014001
[47]. Sangün, L., Guney, O. I., Ozalp, P., & Basusta, N. (2020). Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)-LR algorithms. Iranian Journal of Fisheries Science, 19(2), 541-550.
[48]. Serdar, O., Özcan, E. İ., & Aydın, R. (2017). Length-weight and length-length relationships of Alburnus mossulensis and Acanthobrama marmid (Heckel, 1843) in the Karasu River (Turkey). Yunus Arastirma Bülteni, 17(2), 171-176. https://doi.org/10.17693/yunusae.vi.278434
[49]. Sparre, P., & Venema, S. C. (1998). Introduction to tropical fish stock assessment. Part 1. Manual. In FAO Fisheries Technical Paper. No. 306.1 Rev.2. FAO.
[50]. Suicmez, M., Yilmaz, S., & Seherli, T. (2011). Age and Growth Features of Chondrostoma regium (Heckel, 1843) from Almus Dam Lake, Turkey. Süleyman Demirel Üniversitesi Journal of Science, 6(2), 82-90.
[51]. Tesch, F. W. (1968). Age and Growth. In Methods for Assement of Fish Production in freshwater, Edited by, W. E. Ricker. IBP. Handbook No:3, Blackwell Scientific Publ. Oxford and Edinburg, 93 - 123.
[52]. Türeli Bilen, C., Kokcu, P., & Ibrikci, T. (2011). Application of artificial neural networks (ANNs) for weight predictions of blue crabs (Callinectes sapidus Rathbun, 1896) using predictor variables. Mediterranean Marine Science, 12(2), 439-446. https://doi.org/10.12681/mms.43
[53]. Türkmen, M., & Akyurt, İ. (2000). Karasu Irmağı’nın Aşkale Mevkiinden yakalanan gümüş balığı (Chalcalburnus mossulensis, Heckel 1843)’nın popülasyon yapısı ve büyüme özellikleri. Turkish Journal of Biology, 24, 95-111.
[54]. Türkmen, M., Başusta, N., & Demirhan, S. A. (2005). In M. Karataş (Ed.), Balıklarda Yaş Tayini (pp. 121-148). Balık Biyolojisi Araştırma Yöntemleri.
[55]. URL-1. (2023). https://www.google.com/maps/place/MunzurIrmağı, 09.06.2023.
[56]. von Bertalanffy, L. (1938). A Quantitative Theory of Organic Growth (Inquiries on Growth Laws. II). Human Biology, 10(2), 181-213.
[57]. Yıldırım, A., Haliloğlu, H. İ., Türkmen, M., & Erdoğan, O. (2003). Age and growth characteristics of Chalcalburnus mossulensis (Heckel, 1843) living in Karasu River (Erzurum-Turkey). Turkish Journal of Veterinary and Animal Sciences, 27(5), 1091-1096.
[58]. Yakut, Ü. S. (2019). Keban Baraj Gölü Alburnus mossulensis (Heckel, 1843) popülasyonunda büyüme parametrelerinin belirlenmesi. (557654) Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp
[59]. Yüksel, Y. A., Bozkurt, R., Dörtbudak, M. Y., & Yalçın, H. (2021). Length-Weight Relationship and Condition Factor of Alburnus sellal Heckel, 1843 Population in the Tigris River (Şırnak-Turkey). Commagene Journal of Biology, 5(2), 131-135. https://doi.org/10.31594/commagene.907294
[60]. Witt, S. F., & Witt, C. (1992). Modeling and Forecasting Demand in Tourism. Academic Press.
[61]. Wootton, R. J. (1998). Ecology of Teleost Fishes. Chapman and Hall, London, UL., Pages:404.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-2c79d1a7-88f6-4a8f-a622-9e53aa04323a