Rainfall Forecasting with LSTM by Combining Cloud Image Feature Extraction with CNN and Weather Information

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.1541/ieejjia.23002926

Ryosuke Sato, Yasutaka Fujimoto

引用次数: 0

Abstract

Concern about rainfall increase due to climate change and other factors is growing, inexpensive and easy-to-use rainfall forecasting methods are required. Therefore, this study developed a rainfall forecasting model using a neural network that uses readily available weather information such as cloud images, precipitation, and humidity. The proposed model achieved 89% accuracy for 24-hour-ahead classification, exceeding the 85% accuracy of the Japan Meteorological Agency.(JMA) In addition, by focusing on the seasonality of weather and introducing time information into the forecast model, the stability of the forecast was improved. Finally, a rainfall forecast model was developed and simulated by applying AdaBelief to EfficientNetV2+Bi-LSTM. Consequently, the accuracy of both 2-hour and 24-hour-forecasts exceeded the forecast precision of the previous study and the JMA. In particular, the 24-hour-ahead rainfall forecast precision was improved by more than 10% compared to the previous research, indicating a significant improvement in precision.

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结合云图特征提取、CNN和天气信息的LSTM降水预报

人们越来越关注由于气候变化和其他因素导致的降雨增加，因此需要廉价和易于使用的降雨预报方法。因此，本研究开发了一个使用神经网络的降雨预报模型，该模型使用了现成的天气信息，如云图、降水和湿度。该模式对24小时前分类的准确率达到89%，超过日本气象厅(JMA) 85%的准确率。此外，通过关注天气的季节性，在预报模式中引入时间信息，提高了预报的稳定性。最后，将adabbelieve应用于EfficientNetV2+Bi-LSTM，建立了降雨预报模型并进行了仿真。因此，2小时预报和24小时预报的精度都超过了前人研究和JMA的预报精度。其中，24小时前降水预报精度较以往提高10%以上，精度显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.