气候变化领域集成服务门户(CCPortal): Prediction of daily river water temperatures using an optimized model based on NARX networks

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DOI	10.1016/j.ecolind.2024.111978
	Prediction of daily river water temperatures using an optimized model based on NARX networks
	Sun, Jiang; Di Nunno, Fabio; Sojka, Mariusz; Ptak, Mariusz; Luo, You; Xu, Renyi; Xu, Jing; Luo, Yi; Zhu, Senlin; Granata, Francesco
发表日期	2024
ISSN	1470-160X
EISSN	1872-7034
起始页码	161
卷号	161
英文摘要	Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year ( DOY ) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARXbased model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.
英文关键词	River water temperature; Modeling; NARX; Air2stream; Climate change
语种	英语
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS记录号	WOS:001223146600001
来源期刊	ECOLOGICAL INDICATORS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/289567
作者单位	Yangzhou University; University of Cassino; Poznan University of Life Sciences; Adam Mickiewicz University; Yunnan Normal University
推荐引用方式 GB/T 7714	Sun, Jiang,Di Nunno, Fabio,Sojka, Mariusz,et al. Prediction of daily river water temperatures using an optimized model based on NARX networks[J],2024,161.
APA	Sun, Jiang.,Di Nunno, Fabio.,Sojka, Mariusz.,Ptak, Mariusz.,Luo, You.,...&Granata, Francesco.(2024).Prediction of daily river water temperatures using an optimized model based on NARX networks.ECOLOGICAL INDICATORS,161.
MLA	Sun, Jiang,et al."Prediction of daily river water temperatures using an optimized model based on NARX networks".ECOLOGICAL INDICATORS 161(2024).