Abstract
Natural disasters (e.g. floods, landslides and tsunamis) are phenomena that occur in several countries and cause a great deal of damage, as well as a serious loss of life and materials. Although very often these events cannot be avoided, their environments can be monitored and thus predictions can be made about their likely occurrence so that their effects can be mitigated. One feasible way of carrying out this monitoring is through the use of wireless sensor networks (WSNs) since these disasters usually occur in hostile environments where there is a lack of adequate infrastructure. This article examines the most recent results obtained from the use of machine learning techniques (ML) and adopts a distributed approach to predict floods using a WSN deployed in Brazil to monitor urban rivers. It also conducts a comparative analysis of ML techniques (e.g. Artificial Neural Networks and Support Vector Machines) for the task of flood prediction and discusses the results obtained from each type of technique explored so far. Finally, in the discussion of the results, a suggestion is made about how to improve accuracy in forecasting floods by adopting a distributed approach, which is based on allying computing intelligence with WSNs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basha, E.A., Ravela, S., Rus, D.: Model-based monitoring for early warning flood detection. In: Proceedings of 6th ACM SenSys (2008)
Damle, C., Yalcin, A.: Flood prediction using time series data mining. J. Hydrol. 333(2–4), 305–316 (2007)
Guha-Sapir, D., Hoyois, P., Below, R.: Annual disaster statistical review 2013: the numbers and trends. Technical report, Centre for Research on the Epidemiology of Disasters (2014)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Hossain, M., Turna, T., Soheli, S., Kaiser, M.: Neuro-fuzzy(nf)-based adaptive flood warning system for bangladesh. In: ICIEV International Conference (2014)
Kar, A., Winn, L., Lohani, A., Goel, N.: Soft computing-based workable flood forecasting model for ayeyarwady river basin of myanmar. J. Hydrol. Eng. 17(7), 807–822 (2012)
Takens, F.: Detecting strange attractors in turbulence. Dyn. Syst. Turbul. 898, 366–381 (1981)
Ueyama, J., Hughes, D., Man, K.L., Guan, S., Matthys, N., Horre, W., Michiels, S., Huygens, C., Joosen, W.: Applying a multi-paradigm approach to implementing wireless sensor network based river monitoring. In: 2010 CDEE International Symposium (2010)
Wallemacq, P., Herden, C., House, R.: The human cost of natural disasters 2015: a global perspective. Technical report, Centre for Research on the Epidemiology of Disasters (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Furquim, G., Pessin, G., Gomes, P.H., Mendiondo, E.M., Ueyama, J. (2015). A Distributed Approach to Flood Prediction Using a WSN and ML: A Comparative Study of ML Techniques in a WSN Deployed in Brazil. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-24834-9_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24833-2
Online ISBN: 978-3-319-24834-9
eBook Packages: Computer ScienceComputer Science (R0)