WSN and Fuzzy Logic for Flash Flood and Traffic Congestion Detection

Shitsukane Aggrey Shisiali, Sylvester Walusala W


Floods are the most common natural disaster and source of significant damage to life, agriculture and economy. Flash Floods are particularly deadly because of short timescales on which they occur. Most flood casualties are caused by a lack of information. There is no dedicated flood sensing systems that monitor propagation of flash floods in cities. .Human being do not have power to totally uproot natural calamity but they can predict natural calamity & take major steps to prevent it. Wireless Sensor Network (WSN) and Internet of Things (IoT) technology is used for predicting & detecting flooding condition in this study. WSN is preferred due to its cost effectiveness, faster transfer of data & accurate computation of required parameter for flood prediction. IoT combines embedded system hardware techniques along with data science or machine learning models. The model uses a mesh network connection over ZigBee for the WSN to collect data, and a GPRS module to send data to the internet. Data sets are evaluated using fuzzy logic to detect floods then broadcast alerts. Floods rarely occur hence the system is dedicated for traffic congestion notifications.


Flash flood; Wireless Sensor Network; Internet of Things; fuzzy logic.

Full Text:



D. Evans, “The Internet of Things - How the Next Evolution of the Internet is Changing Everything,” CISCO white Pap., no. April, pp. 1–11, 2011.

L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Comput. Networks, vol. 54, no. 15, pp. 2787–2805, 2010.

J. Radak, B. Ducourthial, V. Cherfaoui, and S. Bonnet, “Ad-hoc Networks and Wireless,” Ad-hoc Networks Wirel. 2014 - Int. Work. Wirel. Sensor, Actuator Robot Networks (WiSARN 2014), vol. 8629, pp. 27–34, 2015.

S. Yinbiao and K. Lee, “Internet of Things : Wireless Sensor Networks Executive summary,” 2014.

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Commun. Mag., vol. 40, no. 8, pp. 102–105, 2002.

Milly, P. C. D., Wetherald, R. T., Dunne, K. A., & Delworth, T. L. (2002). Increasing risk of great floods in a changing climate. Nature, 415(6871), 514-517.

M. Castillo-Effer, D. H. Quintela, W. Moreno, R. Jordan, and W. Westhoff, “Wireless sensor networks for flash-flood alerting,” in Proc. Proc. 5th IEEE Int. Caracas Conf. Devices, Circuits Syst., vol. 1, Nov. 2004, pp. 142–146.

E. Kuantama, L. Setyawan, and J. Darma, “Early flood alerts using short message service (SMS),” in Proc. Int. Conf. Syst. Eng. Technol. (ICSET), 2012, pp. 1–5.

C. L. Lai, J. C. Yang, and Y. H. Chen, “A real time video processing based surveillance system for early fire and flood detection,” in Proc. IEEE Instrum. Meas. Technol. Conf. (IMTC), May 2007, pp. 1–6.

H. Fu et al., “Implementation and characterization of liquid-level sensor based on a long-period fiber grating Mach–Zehnder interferometer,” IEEE Sensors J., vol. 11, no. 11, pp. 2878–2882, Nov. 2011.

N.-B. Chang and D.-H. Guo, “Urban flash flood monitoring, mapping and forecasting via a tailored sensor network system,” in Proc. IEEE Int. Conf. Netw., Sens. Control (ICNSC), Apr. 23–26, 2006, pp. 757–761.

D'Addabbo, A., Refice, A., Pasquariello, G., Lovergine, F. P., Capolongo, D., & Manfreda, S. (2016). A bayesian network for flood detection combining SAR imagery and ancillary data. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3612-3625.

Mousa, M., & Claudel, C. (2014, April). water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning. In Proceedings of the 13th international symposium on Information processing in sensor networks (pp. 277-278). IEEE Press.

Singh, A., & Singh, K. K. (2016). Satellite Image classification using Genetic Algorithm trained Radial Basis Function neural network, application to the detection of flooded areas. Journal of Visual Communication and Image Representation.

Beven, K. J., Kirkby, M. J., Schofield, N., & Tagg, A. F. (1984). Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. Journal of Hydrology, 69(1-4), 119-143.

Rezaeianzadeh, M., Tabari, H., Yazdi, A. A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37.

Seal, V., Raha, A., Maity, S., Mitra, S. K., Mukherjee, A., & Naskar, M. K. (2012). A simple flood forecasting scheme using wireless sensor networks. arXiv preprint arXiv:1203.2511.

TAKASAO, T., & SHIBA, M. (1984). Development of techniques for on-line forecasting of rainfall and flood runoff. Natural disaster science, 6(2), 83-112.

Mitra, P., Ray, R., Chatterjee, R., Basu, R., Saha, P., Raha, S., ... & Saha, S. (2016, November). Flood forecasting using Internet of things and artificial neural networks. In Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016 IEEE 7th Annual (pp. 1-5). IEEE.

Castillo-Effer, M., Quintela, D. H., Moreno, W., Jordan, R., & Westhoff, W. (2004, November). Wireless sensor networks for flash-flood alerting. In Devices, Circuits and Systems, 2004. Proceedings of the Fifth IEEE International Caracas Conference on (Vol. 1, pp. 142-146). IEEE.

Basha, E. A., Ravela, S., & Rus, D. (2008, November). Model-based monitoring for early warning flood detection. In Proceedings of the 6th ACM conference on Embedded network sensor systems (pp. 295-308). ACM.

M. S. Baharum, R. A. Awang, and N. H. Baba, “Flood monitoring system (myfms),” in 2011 IEEE International Conference on System Engineering and Technology (ICSET), (Shah Alam), pp. 204–208, IEEE, 06 2011.

P. B. Bedient, A. W. Holder, and B. E. Vieux, “A radar-based flood alert system (fas) designed for houston, texas,” in Ninth International Conference on Urban Drainage (9ICUD), 2002.

. Jong-uk Lee, Jae-Eon Kim, Daeyoung Kim, and Poh Kit Chong,” RFMS: Real-time Flood Monitoring System with Wireless Sensor Networks,” Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE International Conference on Sept. 29 2008-Oct. 2 2008

Gustavo Furquim, Filipe Neto, Gustavo Pessin, Jó Ueyama, João P. de Albuquerque, Maria Clara, Eduardo M. Mendiondo, Vladimir C. B. de Souza, Paulo de Souza, Desislava Dimitrova, Torsten Braun, “Combining Wireless Sensor Networks and MachineLearning for Flash Flood Nowcasting,”Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International, May 2014

Lu, X., & Brelsford, C. (2014). Network structure and community evolution on twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami. Scientific reports, 4.

Singh, T. N., Singh, R., Singh, B., Sharma, L. K., Singh, R., & Ansari, M. K. (2016). Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, India. Natural Hazards, 81(3), 2019-2030.


Comments on this article

View all comments





About IJC | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJC is published by (GSSRR).