The role of hyperparameters in predicting rainfall using n-hidden-layered networks

Weather prediction is one of the challenging issues around the world. It is necessary to determine the effective use of water resources and forecasting weather-related disasters. The emerging machine learning techniques are coupled with the large set of weather dataset to forecast weather. Rainfall depends on a lot of weather attributes. The dataset may have relevant and irrelevant attributes. In this paper, two supervised learning algorithms are proposed to forecast the weather. In the first method, selected features are fed into the multiple linear regression model for training. Then, the prediction is performed with good accuracy of 82%. In the second method, to reduce the error rate of the deep learning algorithm we need to encode the cyclical features before applying the deep learning algorithm. Then, tuning hyperparameters in the n-hidden-layered networks improved the performance of the model with good accuracy of 92.32%.

Automated summary

In this paper, machine learning techniques are utilized to forecast weather with the proposal of two supervised learning algorithms. The first algorithm uses multiple linear regression model for training with an accuracy of 82%; the second algorithm requires encoding features before applying the deep learning algorithm, and tuning hyperparameters in the n-hidden-layered networks improved the model performance with an accuracy of 92.32%.