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پیش‌بینی روند تغییرات صید ماهی تون زرد باله (Thunnus albacares Bonnaterre, 1788) در آبهای جنوبی کشور براساس مدل‌های آریما (ARIMA) و شبکة عصبی (NN) | ||
| شیلات | ||
| دوره 76، شماره 4، دی 1402، صفحه 579-591 اصل مقاله (1.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jfisheries.2023.360328.1390 | ||
| نویسندگان | ||
| سید احمد رضا هاشمی* 1؛ مسطوره دوستدار2 | ||
| 1مرکز تحقیقات شیلاتی آبهای دور، موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، چابهار، ایران | ||
| 2موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| چکیده | ||
| هدف از این مطالعه، توسعة مدل‌های مختلف پیش‌بینی آبزیان بوده و تلاش شده میزان صید ماهی تون باله زرد (گیدر) در آبهای جنوبی کشور با حداقل خطاهای احتمالی را پیش‌بینی نماید. میانگین صید (Yi±S.D) و لگاریتم صید (LogYi±S.D) برای سال‌های 1376 تا 1400 به‌ترتیب 13744±35378 تن (95% حدود اطمینان 49129–21634 تن) و 0/18±4/51 تن (95% حدود اطمینان 4/69-4/33 تن) بود. براساس آزمون من‌کندال، میانگین صید به‌صورت معنی‌داری طی دورة یاد شده (بیش از دو دهة گذشته) افزایش یافته است (3/8, Z= P<0.05). مدل‌ها پیش‌بینی ترکیبی مختلف آریما (ARIMA, (p, d, q)) براساس شاخص AIC امتحان شد و آریما مدل (0و 0 و1) بهترین تناسب را با روند تغییرات ماهی تون زرد باله یا گیدر در آبهای جنوب کشور داشت (24-=AIC). نتایج و خطای مدل‌های شبکة عصبی (NN) نشان می‌دهد که شبکه‌های عصبی پیش‌خور (FFNN) نسبت به سایر مدل‌ها عملکرد بهتری داشته و مقادیر صید ماهی گیدر را با خطای کمتری شبیه‌سازی و پیش‌بینی می‌کند (0/02 MAE= و 0/03=RMSE). همچنین با توجه به نتایج مدل‌های سری زمانی آریما و شبکة عصبی می‌توان نتیجه گرفت که شبکه‌های عصبی پیش‌خور با دقت بالاتری نسبت به مدل‌های سری زمانی میزان صید ماهی گیدر را شبیه‌سازی می‌کنند و بازگو کنندة آیندة صید ماهی گیدر باشند. به‌نظر می‌رسد پیش‌بینی روند صید آبزیان می‌تواند ابزار مهم مدیران و برنامه‌ریزان شیلاتی برای مدیریت بهتر و پایدار در ذخایر آبزیان بوده و بایستی بیشتر به آن توجه داشت. | ||
| کلیدواژه‌ها | ||
| ماهی گیدر؛ دریای عمان؛ مدل آریما؛ مدل شبکة عصبی | ||
| عنوان مقاله [English] | ||
| Prediction of yellowfin tuna (Thunnus albacares Bonnaterre, 1788) catch trend in the southern waters of the country based on ARIMA and neural network (NN) models | ||
| نویسندگان [English] | ||
| Seyed Ahmad Reza Hashemi1؛ Mastooreh Doustdar2 | ||
| 1Offshore Fisheries Research Center, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Chabahar, Iran | ||
| 2Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran | ||
| چکیده [English] | ||
| The aim of this study is to develop different models of aquatic forecasting and try to predict yellowfin tuna catch in the southern waters of the country with minimum possible errors. The average catch (Yi ± S. D) and logarithm of catch (LogYi ± S. D) for the years 1997 to 2021 were 35,378 ± 13,744 tons (95% confidence interval 21,634 - 49,129 tons) and 4.51 ± 0.18 tons (95% CI The confidence interval was 4.33-4.69 tons), respectively. According to the Mann-Kendall test, the average catch has increased significantly during the mentioned period (over the last two decades) (Z = 3.80, P < 0.05). Different ARIMA combined prediction models (ARIMA, (p, d, q)) were tested based on the AIC index, and the ARIMA model (1, 0, 0) had the best fit with the change trend of yellowfin tuna in the southern waters of the country (AIC =- 24). The predict of yellowfin tuna catch results in the neural network (NN) models was show that feed forward neural networks (FFNN) have better performance than other models and with less error (MAE=0.02 and RMSE=0.03). Also, according to the results of ARIMA time series and neural network models, it can be concluded that feed forward neural networks simulate catch this species fish with higher accuracy than time series models. It seems, forecasting the trend of aquatic catch can be an important tool for fisheries managers and planners for better and sustainable management of aquatic resources and should be given more attention. | ||
| کلیدواژه‌ها [English] | ||
| Yellowfin tuna, Oman Sea, ARIMA models, Neural Network (NN) | ||
| مراجع | ||
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