Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow

• Published in: Journal of hydrology (Amsterdam) 2020-07, Vol.586, p.124896, Article 124896
• Main Authors: Osman, Abdalla, Afan, Haitham Abdulmohsin, Allawi, Mohammed Falah, Jaafar, Othman, Noureldin, Aboelmagd, Hamzah, Firdaus Mohamad, Ahmed, Ali Najah, El-shafie, Ahmed
• Format: Article
• Language: English
• Subjects: Aswan High Dam; Multi-lead forecasting; Nonlinear system identification; River Nile; Streamflow forecasting
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.124896

[Display omitted] •Investigate the potential of Fast-Orthogonal Search (FOS) for streamflow forecasting.•Utilize FOS identification technique to provide a wider selection of candidate.•Examine several training approaches for developing robust forecasting model.•Compare the FOS based model to existing AI based models.•Demonstrate the performance using realistic data from the Nile river in Egypt. Data-driven models for streamflow forecasting have attracted considerable attention, as they are independent of physical system features. The physical features of the river basin are extremely hard to collect, especially for large rivers. Empirical data-driven models, such as stochastic and regression models, have been widely used in the field of streamflow forecasting. However, they suffered limited accuracy in predicting extreme streamflow. They also required raw data pre-processing prior to the modeling process, especially for lengthy data records and for large time-scale increments (e.g. monthly resolution). To overcome these challenges, data-driven forecasting models based on Artificial Intelligence (AI) have been widely used and resulted in enhancing the forecasting accuracy. Nevertheless, AI-based models required augmentation with proper optimization schemes to adjust the model parameters for optimal accuracy. Furthermore, in some cases, due to unsuitability of the optimization model, there is high possibility for overfitting of the AI model, which might cause poor prediction of input patterns that were not adequately mimicked. This study introduces a new approach to streamflow forecasting based on nonlinear system identification. The proposed technique employs Fast Orthogonal Search (FOS) to develop a nonlinear model of stream flow. The main advantage of using FOS is eliminating the requirement of raw data pre-processing and the need for an optimization scheme for model parameter adjustment since the FOS algorithm takes this into account while building the model. In addition, the FOS algorithm includes a pole-zero cancellation procedure that can detect and avoid the over-fitted models. The FOS-based nonlinear modeling approach was adopted in this research for the development of a streamflow forecasting model at Aswan High Dam using monthly basis natural streamflow records for 130 years. The results indicated that the proposed FOS algorithm outperformed the previously developed AI models of streamflow forecasting for large data records and for large time-scal