A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations

Neuro-fuzzy models have been used to predict runoff from rainfall, a hydrological phenomenon associated with a degree of uncertainty. However, rainfall can be measured from different meteorological stations, and runoff forecasting can be biased. Thus, the aim of this work is to propose a new stacking neuro-fuzzy framework for predicting runoff from physically distributed meteorological stations. As a method to estimate single one-day-ahead runoff and as a stacking approach, the Self-Identification Neuro-fuzzy Inference model (SINFIM) and Self-Organizing Neuro-fuzzy Inference System (SONFIS) were developed, respectively. As a case study, data from two Chilean watersheds (the Diguillín River (Ñuble region) and Colorado River (Maule region)) and average daily runoff and average daily rainfall recorded over eighteen years were collected from the Chilean Directorate of Water Resources (DGA). The experimental results show good adjustment in the single forecasting of runoff with meteorological stations showing adjustment and efficiency indexes of greater than 80% in the validation set and being able to efficiently predict both high and low runoff values. However, better results were obtained with the stacking model with values being higher than single runoff predictions and those of state-of-art approaches. Therefore, the general framework proposed represents a good approach for forecasting runoff since it can improve predictions and generate more accurate runoff values than single models.