Modelling daily water temperature from air temperature for the Missouri River

PeerJ

article

Senlin Zhu¹  Emmanuel Karlo Nyarko²  Marijana Hadzima-Nyarko³ 

[1] State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute;Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, University J.J. Strossmayer in Osijek;Faculty of Civil Engineering Osijek, J.J. Strossmayer University of Osijek

关键词: Water temperature;    Air temperature;    Machine learning models;    Standard regression models;    Missouri river;   

DOI  :  10.7717/peerj.4894

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air–water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

【 授权许可】

CC BY