Study on Prediction of Similar Typhoons through Neural Network Optimization

Abstract

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

Prediction of Significant Wave Height in Korea Strait Using Machine Learning

“…They also mentioned that these ML models can be applied in areas ranging from simple regression problems to image and voice recognition, language processing, and the medical industry. Moreover, studies have been conducted to detect oil spills using FNN (Kim and Kim, 2017) and to predict the path of a typhoon (Kim et al, 2019), as well as the volume of goods transported using LSTM (Kim and Lee, 2020). Jain and Deo (2006) presented previous studies that have utilized FNN in the field of ocean engineering.…”