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Deep Learning-Based Rainfall Prediction Using Cloud Image Analysis
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DOI：10.1109/TGRS.2023.3263872.png)
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摘要
En 中文
This study presents a new research direction for predicting rainfall amount using cloud image data. Herein, we employ a convolutional neural networks (CNNs) to develop an image-value model from cloud image data collected from May 20, 2020 to October 24, 2020 using the Internet of Things (IoT) sensors installed at two research locations in Seoul, Republic of Korea. First, we refine the dataset using data preprocessing in three steps: 1) day/night discrimination; 2) ratio adjustment; and 3) image augmentation. Second, we construct a binary classification model using one-hot encoding for the existence of rainfall. This reduces no-rain data instances and increases model performance, thereby enabling the model to extract image features. Finally, we develop a CNN-based image-value model for rainfall prediction with a well-organized model configuration. Rainfall existence results derived from the binary classification model used for model input as preprocessed cloud image data. The proposed rainfall prediction model exhibited 85.59% accuracy on cloud images with an average mean squared error (MSE) of 3.05 for observation data under 3 mm/h. In particular, single application of the function that divides Boolean input by the standard deviation of the dataset within each characteristic resulted in a 17% increase in predicted rainfall accuracy. To the best of our knowledge, this is the first study to train CNN model to predict value (rainfall) with matched image data (cloud), which could be denoted as CNN-based image-value model. Notably, the proposed model can be further extended into other image datasets, including rain streaks with various backgrounds under different climatic conditions.
Keyword:
Data models
Meteorology
Cloud computing
Predictive models
Cameras
Sensors
Machine learning
Cloud image
convolution neural networks (CNNs)
deep learning
Internet of Things (IoT) sensor
rainfall prediction
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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