Implementation of Neural Network for Rainfall Prediction

Many of the technologies nowadays that have helped us solve crucial problems, but some of them need more improvement to prevent miscalculations. One of the crucial problems that I mean is rainfall prediction. The reason why the technologies for rainfall prediction need to be improved is that this problem has a big impact on our lives. A single flaw could lead to a huge flop on many human activities. Many humans have developed techniques and methodologies to improve rainfall predictions, one of them is neural network. 

Neural network works like a human brain, it uses neural units connected to each other to communicate and process data. It's like a computerized form of a human brain because it has a similar architecture, self-learning algorithm, and can be trained to learn many weather conditions, unlike other computers that need to be programmed manually.

Because of how neural network works, it can also be a huge problem if there's a mistake in its development. The developers of this machine need to be really careful about making this thing. This machine also needs to be monitored frequently so it won't do miscalculations. The maker's act and responsibility are key to solving rainfall prediction with neural network.