Abstract

Artificial neural networks (ANN) have attracted significant attention from researchers because many complex problems can be solved by training them. If enough data are provided during the training process, ANNs are capable of achieving good performance results. However, if training data are not enough, the predefined neural network model suffers from overfitting and underfitting problems. To solve these problems, several regularization techniques have been devised and widely applied to applications and data analysis. However, it is difficult for developers to choose the most suitable scheme for a developing application because there is no information regarding the performance of each scheme. This paper describes comparative research on regularization techniques by evaluating the training and validation errors in a deep neural network model, using a weather dataset. For comparisons, each algorithm was implemented using a recent neural network library of TensorFlow. The experiment results showed that an autoencoder had the worst performance among schemes. When the prediction accuracy was compared, data augmentation and the batch normalization scheme showed better performance than the others.

Highlights

  • Accurate weather forecasting is an important issue that plays a significant role in the development of several industrial sectors, such as agriculture and transportation

  • deep neural networks (DNNs) model model are aredescribed, described,which whichwas was trained without the use of any regularization techniques

  • The results of the models trained without the use of any regularization techniques

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Summary

Introduction

Accurate weather forecasting is an important issue that plays a significant role in the development of several industrial sectors, such as agriculture and transportation. Many companies are using weather prediction techniques to analyze consumer demands. Exact forecasting is essential for people to organize and plan their days. It is very difficult to predict the weather precisely because the atmosphere changes dynamically. Physical simulations were the most widely used scheme. With this method, the current atmospheric condition is sampled, and future conditions are predicted by comparing thermodynamic characteristics. Artificial neural networks (ANNs) have been widely used for weather prediction because they perform better through the use of machine learning. The human brain is composed of 100 billion interconnected neurons

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