Multivariable NARX Based Neural Networks Models for Short-Term Water Level Forecasting

Abstract

In this work a novel application for multivariable forecasting is presented, applied to hydrological variables and based on a multivariable NARX model. The proposed approach is designed for two hydrological stations located at the Atrato River in Colombia where the variables of water level, water flow and water precipitation are correlated by using the NARX model based on a neural network structure. The structure of the NARX-based neural network is designed in order to consider the complex dynamics of hydrological variables and their corresponding cross-correlations. A short-term water level forecasting is designed based on the NARX model, to be used as an early warning flood system. The validation of the proposed approach is performed by comparing the estimation error with an ARX dynamic model. As a result, it is shown that a NARX model structure is more suitable for water level forecasting than simplified structures.

Keywords

• forecast
• water level
• neural network