GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK

LIN Yudao; TAO Tao; XIN Kunlun; PU Zhengheng; CHEN Lei

doi:10.13205/j.hjgc.202304021

Volume 41 Issue 4

Apr. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(4): 149-153

LIN Yudao, TAO Tao, XIN Kunlun, PU Zhengheng, CHEN Lei. GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021

Citation:

LIN Yudao, TAO Tao, XIN Kunlun, PU Zhengheng, CHEN Lei. GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021

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GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK

doi: 10.13205/j.hjgc.202304021

College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China

Received Date: 2022-04-21
Available Online: 2023-05-26
Publish Date: 2023-04-01

Abstract

Abstract

Water demand forecasting is the basis of the optimization of water networks, and its accuracy is important for the following work. Graph neural network, an emerging architect, takes advantage of the topological information to enhance the learning results, which means the topology of the water network can an input feature of the graph-based neural network model and lift the prediction accuracy. This work tried to utilize Graph Wavenet model in water demand forecasting and make use of the topological feature of the water network. After utilizing real-world history data in the training process, the average 5 min prediction MAPE error result was 1.28%. Besides, this experiment showed that Graph Wavenet raised the prediction accuracy, compared to WaveNet without graph convolution operation, confirming the contribution of the graph convolution mechanism. This work also talked about future applications of GNN on water distribution networks.
- water demand forecast,
- water distribution network,
- graph neural network,
- time series forecasting

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References(18)

References

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