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River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network

  • Sultan Aljahdali
  • Alaa Sheta
  • Hamza TurabiehEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

Abstract

Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.

Keywords

Forecasting Long short-term memory River Flow 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceTaif UniversityTaifSaudi Arabia
  2. 2.Computer Science DepartmentSouthern Connecticut State UniversityNew HavenUSA
  3. 3.Department of Information TechnologyTaif UniversityTaifSaudi Arabia

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