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مقالات فارسیISIکنفرانسهاژورنالها

Forecasting Short-term Container Vessel Traffic Volume Using Hybrid ARIMA-NN Model

محل انتشار: فصلنامه بین المللی مهندسی سواحل، فراسواحل و محیط زیست، دوره: 3، شماره: 3
سال انتشار: 1398
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 300

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این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:
https://civilica.com/doc/1150157

شناسه ملی سند علمی:

JR_IJCOE-3-3_006

تاریخ نمایه سازی: 15 بهمن 1399

چکیده مقاله:

A combination of linear and non-linear models results in a more accurate prediction in comparison with using linear or non-linear models individually to forecast time series data. This paper utilizes the linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural network (ANN) model to develop a new hybrid ARIMA-ANN model for prediction of container vessel traffic volume. The suggested hybrid method consists of an optimized feed-forward, back-propagation model with a hybrid training algorithm. The database of monthly traffic of Rajaee Port for thirteen years from 2005-2018 is taken into account. The performance of the developed model in forecasting short-term traffic volume is evaluated using various performance criteria such as correlation coefficient (R), mean absolute deviation (MAD), mean squared error (MSE) and mean absolute percentage error (MAPE). The developed model provides useful insights into container traffic behavior. Comparing the results with the real data-sets demonstrates the superior performance of the hybrid models than using models individually in forecasting traffic data.

کلیدواژه ها:

Forecasting Container Traffic ، Neural Network ، ARIMA model ، Hybrid ARIMA-NN Model

نویسندگان

Negar Sadeghi Gargari

Tarbiat Modares University

Hassan Akbari

Tarbiat Modares University

Roozbeh Panahi

Tarbiat Modares University

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله 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 مقاله لینک شده اند :
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  • Lam WH, Ng PL, Seabrooke W, Hui EC., (2004), Forecasts ...
  • Du S, Li T, Gong X, Yang Y, Horng SJ., ...
  • Eslami P, Jung K, Lee D, Tjolleng A., (2017), Predicting ...
  • Wang L, Zou H, Su J, Li L, Chaudhry S., ...
  • Zhang Y, Ye Z., (2008), Short-term traffic flow forecasting using ...
  • Zhang GP., (2003), Time series forecasting using a hybrid ARIMA ...
  • Box GE, Jenkins GM., (1994), Time Series Analysis: Forecasting and ...
  • Jugović A, Hess S, Poletan Jugović T., (2011), Traffic demand ...
  • Beale MH, Hagan MT, Demuth HB., (2010), Neural network toolbox™ ...
  • Agami N, Atiya A, Saleh M, El-Shishiny H., (2009), A ...
  • Skorpil V, Stastny J., (2006), Neural networks and back propagation ...
  • Nunes da Silva I, Hernane Spatti D, Andrade Flauzino R, ...
  • Bergstra J, Bengio Y., (2012), Random search for hyper-parameter optimization. ...
  • Gökkuş Ü, Yıldırım MS, Aydin MM., (2017), Estimation of container ...
  • Zhang G, Patuwo BE, Hu MY., (1998), Forecasting with artificial ...
  • McKenzie S., (2004), Social sustainability: Towards some definitions. Systems Research ...
  • Wang S, Wang S, Gao S, Yang W., (2017), Daily ...
  • 1- Bichou K. (2014 Apr 16) Port operations, planning, and ...
  • Haiyan W, Youzhen W. (2015) Vessel traffic flow forecasting with ...
  • Zissis D, Xidias EK, Lekkas D. (2016), Real-time vessel behavior ...
  • Mostafa MM. (2004), Forecasting the Suez Canal traffic: a neural ...
  • Nicolau V, Aiordachioaie D, Popa R. (2004), Neural network prediction ...
  • Lagerweij R, de Vries G, van Someren M. (2009), Learning ...
  • Zhou Y, Daamen W, Vellinga T, Hoogendoorn SP., (2019), Ship ...
  • Perera LP, Oliveira P, Soares CG., (2012), Maritime traffic monitoring ...
  • Ebada A, Maksoud MA., (2005), Prediction of Ship Turning Manoeuvre ...
  • Simsir U, Ertugrul S., (2009), Prediction of manually controlled vessels' ...
  • Hajbi A., (2011), Traffic forecasting in Moroccan ports; Supply Chain ...
  • Anderson DR, Sweeney DJ, Williams TA, Camm JD, Cochran JJ., ...
  • Zhang W, Zhao S., (2013), Forecasting Research on the Total ...
  • Chou CC, Chu CW, Liang GS., (2008), A modified regression ...
  • Lam WH, Ng PL, Seabrooke W, Hui EC., (2004), Forecasts ...
  • Du S, Li T, Gong X, Yang Y, Horng SJ., ...
  • Eslami P, Jung K, Lee D, Tjolleng A., (2017), Predicting ...
  • Wang L, Zou H, Su J, Li L, Chaudhry S., ...
  • Zhang Y, Ye Z., (2008), Short-term traffic flow forecasting using ...
  • Zhang GP., (2003), Time series forecasting using a hybrid ARIMA ...
  • Box GE, Jenkins GM., (1994), Time Series Analysis: Forecasting and ...
  • Jugović A, Hess S, Poletan Jugović T., (2011), Traffic demand ...
  • Beale MH, Hagan MT, Demuth HB., (2010), Neural network toolbox™ ...
  • Agami N, Atiya A, Saleh M, El-Shishiny H., (2009), A ...
  • Skorpil V, Stastny J., (2006), Neural networks and back propagation ...
  • Nunes da Silva I, Hernane Spatti D, Andrade Flauzino R, ...
  • Bergstra J, Bengio Y., (2012), Random search for hyper-parameter optimization. ...
  • Gökkuş Ü, Yıldırım MS, Aydin MM., (2017), Estimation of container ...
  • Zhang G, Patuwo BE, Hu MY., (1998), Forecasting with artificial ...
  • McKenzie S., (2004), Social sustainability: Towards some definitions. Systems Research ...
  • Wang S, Wang S, Gao S, Yang W., (2017), Daily ...
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