An operational dynamical neuro-forecasting model for hydrological disasters

Abstract

In the last decades, artificial neural network has been increasingly applied in hydrological modeling given its potential to process the complex nonlinear relationships of the associated physical-environmental variables and produce a suitable solution (for instance, a forecasting model) in a relatively short time. In this scope, this work reports the design methodology and the operational results obtained with an artificial neural network-based model developed to forecast, with 2 h in advance, the level of a river in the mountainous region of Rio de Janeiro state in Brazil. This is an area susceptible to natural disasters with recent records of floods and landslides that caused environmental and socio-economic damage of large proportions. The proposed neural network uses an innovative learning algorithm (the quasi-Newton optimization method is applied to the slopes of each hidden activation function) and, as input features, values of rainfall and river level data collected from 8 monitoring stations located on studied watershed between 2013 and 2014. The results of the neural model, with NASH index greater than 0.86, are promising making possible its operational use on an issuing flood alerts system.