River/stream water temperature forecasting using artificial intelligence models: a systematic review

Acta Geophysica

article

Zhu, Senlin¹  Piotrowski, Adam P.³ 

[1] College of Hydraulic Science and Engineering, Yangzhou University;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute;Institute of Geophysics, Polish Academy of Sciences

关键词: River water temperature forecasting;    Artificial intelligence models;    Hybrid model;    Review;   

DOI  :  10.1007/s11600-020-00480-7

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

Water temperature is one of the most important indicators of aquatic system, and accurate forecasting of water temperature is crucial for rivers. It is a complex process to accurately predict stream water temperature as it is impacted by a lot of factors (e.g., meteorological, hydrological, and morphological parameters). In recent years, with the development of computational capacity and artificial intelligence (AI), AI models have been gradually applied for river water temperature (RWT) forecasting. The current survey aims to provide a systematic review of the AI applications for modeling RWT. The review is to show the progression of advances in AI models. The pros and cons of the established AI models are discussed in detail. Overall, this research will provide references for hydrologists and water resources engineers and planners to better forecast RWT, which will benefit river ecosystem management.

【 授权许可】

Unknown