Application of machine learning models based on ANN and GA coupling algorithms in hydrological runoff simulation

Kun Huang; Jing Zhang; Yongyu Song

doi:10.1117/12.2635807

6 May 2022 Application of machine learning models based on ANN and GA coupling algorithms in hydrological runoff simulation

Kun Huang, Jing Zhang, Yongyu Song

Author Affiliations +

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122561K (2022) https://doi.org/10.1117/12.2635807
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

Hydrological forecast technology plays an important role in water resources management for agricultural irrigation, water supply and flood control in urban and rural areas. However, how to improve the applicability of runoff forecast models has always been a major difficulty in water resources research. As the largest water system in North China, the Haihe River Basin has experienced severe water shortages in recent years, which has put tremendous pressure on water supply in northern China. Based on monthly runoff observed at 15 hydrological stations from 2001 to 2018, this paper takes the Haihe River Basin as the study area and applies the simple ANN (artificial neural network) model, the GAANN coupling model, and the SWAT model to forecasting monthly runoff respectively. Four evaluation criteria (R2, NSE, RMSE, MAE) for prediction performance of models are used with a view to discussing the applicability of different models to hydrological forecast of rivers in resource-deficient areas of North China.

Citation Download Citation

Kun Huang, Jing Zhang, and Yongyu Song "Application of machine learning models based on ANN and GA coupling algorithms in hydrological runoff simulation", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122561K (6 May 2022); https://doi.org/10.1117/12.2635807

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