PROPOSING A PRE-PROCESSING THAT CONTRIBUTES TO THE ACCURACY IMPROVEMENT OF LSTM WATER LEVEL PREDICTIONS WITH EXTRACTED, PERIODICAL DATA

• Published in: Artificial Intelligence and Data Science Vol. 3; no. J2; pp. 85 - 91
• Main Authors: KIMURA, Nobuaki, YOSHINA, Ikuo, MINAGAWA, Hiroki, FUKUSHIGE, Yudai, BABA, Daichi
• Format: Journal Article
• Language: Japanese
• Published: Japan Society of Civil Engineers 2022
• ISSN: 2435-9262
• DOI: 10.11532/jsceiii.3.J2_85

This study proposes how to improve the prediction accuracy of deep neural network (DNN) using the feature extraction of frequencies from input data in the preprocessing of the DNN simulation. This method requires the Fourier transform and inverse formation to extract frequency-based features from the data. The features are added into the input data as new data. The method was applied to the water level prediction at a pond nearby a drainage pumping station. The transform technique extracted daily- and half-day- frequencies for the dataset collected at a field for about eight years. This study compared the prediction with both frequency features with that without the features. The results show that the prediction with the proposed method was improved by 3-5% in a root mean square error (RMSE) for 1-6-hour lead time (LT) in a comparison of the conventional method . The future prediction with an extra dataset was also improved up to 4% in RMSE.