Author(s)

Li Wang ¹, Wenhao Li ¹, Xiaoyi Wang ^1,2, Jiping Xu ¹, Zhiyao Zhao ¹, Jiabin Yu ¹, Huiyan Zhang ¹, Qian Sun ¹, Yuting Bai ¹

Affiliation(s)

¹ Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology & Business University, Beijing, China
² Beijing Institute of Fashion Technology, Beijing, China

Corresponding Author

Wenhao Li

ABSTRACT

The current research on cyanobacterial bloom prediction is mainly conducted by collecting data in the field and sending them to the laboratory for analysis to obtain water quality data, this method is affected by the weather and does not show the whole water information. Remote sensing images have been used more and more in water bloom prediction due to their high accuracy and convenient acquisition methods, but the accuracy of existing bloom prediction methods based on remote sensing image is low. To solve this issue, a water bloom prediction model based on convolutional long-short time network and 3DCNN network is proposed in this paper, which can predict the remote sensing images of future time by inputting the remote sensing images of historical time, and the eutrophication level of water body of future time can be obtained by analysing the remote sensing images of future time, and then realize the prediction of cyanobacterial water bloom outbreak. To validate the method proposed in this paper, two-dimensional convolutional neural network and three-dimensional convolutional neural network are used as comparative experiments. The experimental results show that the prediction accuracy of the cyanobacterial bloom prediction model proposed in this paper is significantly better than t other two neural network models, which proves the effectiveness of the method in this paper.

KEYWORDS

water eutrophication, remote sensing image, prediction, convolutional long and short time neural network

CITE THIS PAPER

Li Wang, Wenhao Li, Xiaoyi Wang, Jiping Xu, Zhiyao Zhao, Jiabin Yu, Huiyan Zhang, Qian Sun, Yuting Bai, Remote Sensing Image Prediction of Water Environment Based on 3D CNN and ConvLSTM. Geoscience and Remote Sensing (2022) Vol. 5: 28-39. DOI: http://dx.doi.org/10.23977/geors.2022.050105.

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