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

Abstract

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.

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Acknowledgement

This study was supported by the China Postdoctoral Science Foundation (2018M640499). The research conducted by authors affiliated with the Institute of Geophysics, Polish Academy of Sciences and published in this paper has been financed by the National Science Centre, Poland, grant number 2016/21/B/ST10/02516 (2017–2020) and statutory activities No 3841/E-41/S/2019 of the Ministry of Science and Higher Education of Poland.

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Correspondence to Senlin Zhu.

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Zhu, S., Piotrowski, A.P. River/stream water temperature forecasting using artificial intelligence models: a systematic review. Acta Geophys. 68, 1433–1442 (2020). https://doi.org/10.1007/s11600-020-00480-7

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Keywords

  • River water temperature forecasting
  • Artificial intelligence models
  • Hybrid model
  • Review