AN IOT-ENABLED FLOOD INTENSITY PREDICTION VIA ENSEMBLE MACHINE CODE MODEL

Abstract: Stream flooding is a trademark wonder that can devastatingly influence human life likewise, monetary incidents. There have been various systems in considering stream flooding; in any case, lacking agreement and confined data about flooding conditions defeat the improvement of balance and control measures for this trademark wonder. This includes one more technique for the assumption for water level in relationship with flood earnestness using the gathering model. Our philosophy involves the latest headways in the Internet of Things (IoT) and AI for the robotized assessment of flood data that might be useful to prevent devastating occasions. Investigation results show that gathering learning gives a more strong gadget to expect flood earnestness levels. Keywords: Internet of Things , LSTM ,Machine to Machine Innovation