| 基于图神经网络的地表水水质预测模型 |
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| 引用本文： | 许佳辉,王敬昌,陈岭,吴勇.基于图神经网络的地表水水质预测模型[J].浙江大学学报(自然科学版 ),2021,55(4):601-607. |
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| 作者姓名： | 许佳辉 王敬昌 陈岭 吴勇 |
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| 作者单位： | 1. 浙江大学 计算机科学与技术学院，浙江 杭州 3100272. 浙江鸿程计算机系统有限公司，浙江 杭州 310009 |
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| 基金项目： | “十三五”水体污染控制与治理科技重大专项资助项目（2018ZX07208-009）；中央高校基本科研业务费专项资金资助项目（2020QNA5017） |
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| 摘 要： | 针对水质数据在时间和空间维度上的复杂依赖关系，提出基于图神经网络（GNN）的地表水水质预测模型. 该模型采用GNN建模地表水水质监测站点在空间上的复杂依赖关系，使用长短时记忆网络（LSTM）建模水质指标序列在时间上的复杂依赖关系，将编码结果输入到解码器中得到预测输出. 实验结果表明，与时间序列分析方法、通用回归方法和一般深度学习方法相比，该模型能够实现23.3%、26.6%和14.8%的性能提升.
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| 关 键 词： | 水质预测 图神经网络（GNN） 深度神经网络 长短时记忆网络（LSTM） 深度学习 |
Surface water quality prediction model based on graph neural network |
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| Jia-hui XU,Jing-chang WANG,Ling CHEN,Yong WU.Surface water quality prediction model based on graph neural network[J].Journal of Zhejiang University(Engineering Science),2021,55(4):601-607. |
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| Authors: | Jia-hui XU Jing-chang WANG Ling CHEN Yong WU |
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| Abstract: | A surface water quality prediction model based on graph neural network (GNN) was proposed to solve the problem that water quality data has complex dependencies in both temporal and spatial dimensions. GNN was utilized to model the complex spatial dependencies of monitoring stations, and long short-term memory (LSTM) was used to model the complex temporal dependencies of historical water quality sequences. Then the encoded vector was input into the decoder to get the water quality prediction output. The experimental results show that the model can achieve 23.3%, 26.6% and 14.8% performance improvements compared with time series analysis methods, general regression methods and existing deep learning methods. |
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| Keywords: | water quality prediction graph neural network (GNN) deep neural network long short-term memory (LSTM) deep learning |
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