An Accurate Flood Forecasting Model Using Wireless Sensor Networks and Chaos Theory: A Case Study with Real WSN Deployment in Brazil
Monitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSN) for data collection is a viable method since these domains lack any infrastructure. Further studies are required to handle the data collected to provide a better modeling of behavior and make it possible to forecast impending disasters. These factors have led to this paper which conducts an analysis of the use of data gathered from urban rivers to forecast future flooding with a view to reducing the damage they cause. The data were collected by means of a WSN in São Carlos, São Paulo State, Brazil and were handled by employing the Immersion Theorem. The WSN were deployed by our group in the city of São Carlos due to numerous problems with floods. After discovering the data interdependence, artificial neural networks were employed to establish more accurate forecasting models.
KeywordsWireless Sensor Network Machine Learning Time series Analysis Chaos Theory Modeling Prediction
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