This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Item Type:	Refereed Conference Paper
Keywords:	flash flood nowcasting, machine learning, wireless sensor network
Research Division:	Information and Computing Sciences
Research Group:	Software engineering
Research Field:	Software engineering not elsewhere classified
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	de Souza, P (Professor Paulo de Souza Junior)
ID Code:	118043
Year Published:	2014
Web of Science® Times Cited:	8
Deposited By:	Information and Communication Technology
Deposited On:	2017-07-03
Last Modified:	2017-10-17
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