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

Glauston Roberto Teixeira De Lima,Graziela Balda Scofield

doi:10.1590/2318-0331.262120200152

Glauston Roberto Teixeira De Lima, Graziela Balda Scofield

Open Access

https://doi.org/10.1590/2318-0331.262120200152

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Abstract

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.

Highlights

Every year are recorded in Brazil occurrences of floods in urban areas that cause socioeconomic losses (Banco Mundial, 2012; Haddad & Teixeira, 2015)
The results indicated that deep NN (DNN) performed better than the other methods
In the training algorithm executions that generated the results presented (i) the neural network (NN) weights were initialized with small random values, (ii) the maximum number of epochs was chosen as training stopping criterion and was set at 4000, (iii) the learning rate decayed exponentially during the 4000 epochs from 0.1 to 0.01 and (iv) the parameter (β) of the hidden activation functions was set at 0.1 and for the output activation functions was set at 0.4

Summary

Introduction

Every year are recorded in Brazil occurrences of floods in urban areas that cause socioeconomic losses (Banco Mundial, 2012; Haddad & Teixeira, 2015). Two of these requirements are very important in order to help the municipality’s civil defense to protect the population and reduce economic and material impacts: advance and accuracy (International Strategy for Disaster Reduction, 2006; International Network for Multi-Hazard Early Warning Systems, 2017). Considering the rainfall forecast approaches most commonly used today, the forecast of this type of rain is more accurate the closer to the moment of its occurrence. This makes the two requirements, (advance and accuracy) to conflict and the task of issue a warning for flash floods meeting both becomes very challenging and it is usually only possible to optimize the tradeoff between them

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Results

Conclusion