深度学习在水文预测领域的应用进展与思考

Application Progress and Thinking of Deep Learning in the Field of Hydrological Prediction

合理的水文预测模型是水文水资源决策管理的基础,如何充分挖掘既有水文数据中的信息成为当前水文预测领域的一大挑战,深度学习方法的快速发展为水文预测提供了新的思路。针对国内外近期提出的水文预测深度学习模型进行归纳总结,从数据来源、方法模型、验证讨论等方面探讨了深度学习在水文预测领域的应用进展,并进一步给出了数据获取、模型迁移、实时预警等方面的思考。结果表明:充足的水文数据是精准预测的前提,合理的模型构建策略是考虑各种不确定因素的关键手段,模型的适用性和实时预警是未来进一步研究的方向。

A reasonable hydrological prediction model is the foundation of hydrological and water resources decision-making and management.How to fully mining the information in existing hydrological data has become a major challenge in the current hydrological prediction field.The rapid development of deep learning methods provides new ideas for hydrological prediction.In view of the recent domestic and foreign hydrological prediction deep learning models,this paper discusses the application progress of deep learning in the field of hydrological prediction from the aspects of data sources,proposed models,verification and discussion,and further provides some suggestions on data acquisition,model migration,and real-time early warning.The results show that sufficient hydrological data is a prerequisite for accurate prediction,a reasonable model building strategy is a key means to consider various uncertain factors,and the applicability of the model and real-time early warning are the directions for further research in the future.