Analysis of Early Warning Tsunami Prediction System using the Arduino Uno R3 and DNN

An early warning tsunami prediction module is a device that predicts the tsunami within a day, the warning system improves the survival rate of the shoreline people and the destruction rate is also reduced. Present system only detect the tsunami and it doesn't forecast any of the warning signals and they are not equipped with the predicting models and they follow the older flow of warning forecasting (i.e.) the data collected from the source is sent to the data centers and they will be deciding the decision which leads to the lesser efficiency in the forecasting. This research study has utilized the DNN architecture to automate every process and boost the performance. It is an AI automated process of detecting the tsunami and broadcasting the distress signal around the area. It records the seismic activity continuously if the threshold is reached according to the position and the winds peed data, the direction of the tsunami is predicted These modules are deployed far away from the shoreline and the maintenance of the module is less. The main goal of this module is to provide warning as earlier as possible so that people can be ready for the impact of tsunami and the survival rate can also increase, the data to be uploaded in the database and additionally the telecast contains the humidity data, temperature data, time series, depth data, global position data, seismic data, turbidity data, this device has been developed with the Atmel microcontroller and equipped with the neural network, which is capable of predicting the Tsunami.