Urban Flood Forecast using Machine Learning on Real Time Sensor Data
All the underpasses, flyovers and drainage networks in the urban areas are designed to manage a maximum rainfall. This situation implies an accepted ﬂood risk for any greater rainfall event. This threat is very often underestimated as components such as climate change is disregarded. But even great structural alterations cannot assure that urban flood control precautions would be able to cope with all future rainfall events. Hence, being readily able to forecast city or urban ﬂoods in real time is one of the main tasks of this forecast. The current Urban flood forecasting methods involve the use of Geographical Information Systems techniques. Even though, these systems allow to detect and model the flood patterns in a larger perspective. They cannot pin point precise location behavior. Machine Learning models in conjunction with a sensor network can be essential elements of urban ﬂood forecast systems, as an active part of the system or as study tools. The paper goes into the application of machine learning models to better predict flood pattern based on several external factors in real time.
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