Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting

Abstract

River stream-flow is well-thought-out as an essential element in the hydrology studies, especially for reservoir management. Forecasting river stream-flow is the key for the hydrologists in proposing certain short or long-term planning and management for water resources system. In fact, developing stream-flow forecasting models are generally categorized into two main classes; process and data-driven model. Different model techniques based on empirical methods, such as stochastic model or regression model, more recently, Artificial Intelligent (AI) models have been examined and could provide accurate stream-flow forecasting. However, AI models experienced crucial difficulty is the necessity to utilize appropriate pre-processing methods for the raw data. In addition, the AI model should be augmented with proper optimization model to adjust the model parameters to achieve the optimal accuracy. In this paper, a novel model namely; Fast Orthogonal Search (FOS) model is proposed to develop river stream-flow forecasting. FOS is basically structured for recognizing the difference equation and its functional expression model for the mapping between the model input and output. The major advantage of using FOS is the waiver of the requirement of data pre-processing and optimization model for model parameters adjustment as these procedures are performed implicitly inside FOS. In addition, pole-zero cancellation procedure within FOS process can detect the over-fitted models and avoid them. The proposed FOS method was adopted in this research to perform stream-flow forecasting model at Aswan High Dam using monthly basis for130 years. Results showed outstanding performance for stream-flow forecasting accuracy compared to other AI models developed during the last 10 years.