Physics-guided spatio–temporal neural network for predicting dissolved oxygen concentration in rivers

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2024-04-22 DOI:10.1080/13658816.2024.2342321

Qiliang Liu, Yuzhao Li, Jie Yang, Min Deng, Junjie Li, Keyi An

引用次数: 0

Abstract

The prediction of river water quality is key in water resource management. Data-driven machine learning models have been widely used for predicting river water quality. However, these models seldom...

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用于预测河流溶解氧浓度的物理引导时空神经网络

河流水质预测是水资源管理的关键。数据驱动的机器学习模型已被广泛用于预测河流水质。然而，这些模型很少...

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来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.