Forecasting the catch of kilka species (Clupeonella spp.) using Time Series SARIMA models in the Southern Caspian Sea
Publish place: Caspian Journal of Enviromental Sciences، Vol: 16، Issue: 4
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
View: 53
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_CJES-16-4_004
تاریخ نمایه سازی: 27 خرداد 1403
Abstract:
Fisheries management receives assistance by prediction of events to evaluate fluctuating values for a target species to formulate proper policies and actions particularly for threatened and endangered species. This study aimed to predict ۷ years Catch Per Unit Effort (CPUE) of kilka fishes as at-risk population in southern regions of the Caspian Sea. The former catch data from the Fisheries Organization of Iran (IFO) archives (۱۹۹۷ to ۲۰۱۴) were analyzed using ARIMA and SARIMA models. The data were divided into four parts (quarters) addressing one-fourth of a year to represent time and expressed as “Q”. According to periodic changes of ACF and PACF indices, seasonal ARIMA (SARIMA) models were used. The appropriate SARIMA models were examined using BIC, RMSE, R۲, MSE and Ljung-Box indices. SARIMA (۰, ۱, ۱) × (۰, ۱, ۱) ۴ process was the selected final model which met the criterion of model parsimony according to BIC of ۳۱.۹۱, RMSE of ۷۱۹۵۱۹۳ , MAE of ۴۳۷۲۱۷۸ , R۲ of ۰.۸۲ and Ljung-Box index < ۰.۰۵. Based on selected SARIMA model, the forecasts indicated that if the fishing fleet and efforts remain at the present level, the performance of kilka fishing will likely have gentle rise by ۲۰۲۱.
Keywords:
Authors
K Amiri
Department of Biology, Faculty of Science, University of Guilan, Rasht, Iran
N Shabanipour
Department of Marine Sciences, The Caspian Sea Basin Research Centre, University of Guilan, Rasht, Iran
S Eagderi
Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :