Feasibility study on operational use of neural networks in a flash flood early warning system

Abstract

Issuing early and accurate warnings for flash floods is a challenge when the rains that deflagrate these natural hazards occur on very short space-time scales. This article reports a case study in which a neural network-based hydrological model is designed to forecast one hour in advance if the water level in a small mountain watershed with short time to peak, situated in the city of Campos do Jordão in Brazil, will exceed its attention quota. This model can be a powerful auxiliary tool in a flash flood early warning system, since with it decision-making becomes semi-automated, making it possible to improve the warnings advance-accuracy tradeoff. A deep-learning neural network using Exponential Linear Unit activation functions was designed based on 3-years rainfall and water level data from 11 hydrometeorological stations of the National Centre for Monitoring and Early Warning of Natural Disasters. In the training of the neural network, two combinations of input variables were tested. The tuples in the test set were classified through voting with 60 classifiers. The first results obtained in Matlab environment with high percentages of true positives indicate that it is feasible to use the neural model operationally.