Zhu Senlin, Nyarko Emmanuel Karlo, Hadzima-Nyarko Marijana
State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nnajing, China.
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, University J.J. Strossmayer in Osijek, Osijek, Croatia.
PeerJ. 2018 Jun 7;6:e4894. doi: 10.7717/peerj.4894. eCollection 2018.
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.
河流的生化和物理特性直接受水温影响，进而影响水生生态系统的整体健康状况。准确估算水温是个复杂问题。河流水温建模通常基于合适的数学模型以及对各种大气因素的实地测量。本文通过开发三种不同的机器学习模型（人工神经网络（ANN）、高斯过程回归（GPR）和自助聚合决策树（BA-DT））来研究密苏里河的气-水温关系。还开发了标准模型（线性回归、非线性回归和随机模型）并与机器学习模型进行比较。分析这三种标准模型，随机模型明显优于标准线性模型和非线性模型。所有三种机器学习模型结果相当且优于随机模型，其中GPR对2号和3号站点的结果略好，而BA-DT对1号站点的结果略好。机器学习模型是可用于预测每日河流水温的非常有效的工具。
