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Secchi Disk Depth Estimation from Water Quality Parameters: Artificial Neural Network versus Multiple Linear Regression Models?

机译：根据水质参数估算Secchi圆盘深度：人工神经网络与多元线性回归模型？

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摘要

In the present investigation, a new model based on feedforward neural networks (FFNN) is developed and compared to the standard multiple linear regression (MLR) in modeling Secchi disk depth (SD) in the Saginaw Bay, Lake Huron, Michigan, USA. The model uses four water quality parameters as input, namely total suspended solids (TSS), water temperature (TE), dissolved oxygen (DO) and chlorophyll (Chl). In an attempt to identify the important parameters that influence the SD, four water quality parameters were selected for further investigation. The analysis identified TSS and Chl to have the most important influence on the SD; and the inclusion of DO and TE did not lead to an overall improvement in the performance of the models. The FFNN and MLR were evaluated using well-known statistical indices, i.e., the correlation coefficient (CC), the root mean squared error (RMSE) and the mean absolute error (MAE). The results obtained from the present investigation are very promising, as we demonstrated that the Secchi disk depth can be predicted very well with correlation coefficient equal to 0.918 in the testing phase.
机译：在本研究中，开发了一种基于前馈神经网络（FFNN）的新模型，并将其与标准多元线性回归（MLR）在美国密歇根州休伦湖萨吉诺湾的Secchi盘深度（SD）建模中进行了比较。该模型使用四个水质参数作为输入，即总悬浮固体（TSS），水温（TE），溶解氧（DO）和叶绿素（Chl）。为了确定影响SD的重要参数，选择了四个水质参数进行进一步研究。分析确定TSS和Chl对SD的影响最大；而包含DO和TE并没有导致模型性能的整体改善。 FFNN和MLR使用众所周知的统计指标进行评估，即相关系数（CC），均方根误差（RMSE）和平均绝对误差（MAE）。从本次调查中获得的结果是非常有希望的，因为我们证明了在测试阶段可以很好地预测Secchi盘深度，相关系数等于0.918。

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