River/stream water temperature forecasting using artificial intelligence models: a systematic review
| Autor: | Senlin Zhu, Adam P. Piotrowski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
River water temperature
River ecosystem 010504 meteorology & atmospheric sciences business.industry Process (engineering) 010502 geochemistry & geophysics 01 natural sciences River water Current (stream) Water resources Geophysics Water temperature Environmental science Artificial intelligence Applications of artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | Acta Geophysica. 68:1433-1442 |
| ISSN: | 1895-7455 1895-6572 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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