Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Abstract

In this study, a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river. The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath River at Railroad Bridge at Lake Ewauna. The input variables used for the two models are water, pH, temperature, electrical conductivity, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), the mean absolute error (MAE), Willmott's index of agreement (d), and correlation coefficient (CC) statistics. Of the two approaches employed, the best fit was obtained using the GRNN model with the four input variables used.

Keywords:

• generalized regression neural networks
• GRNN
• multiple linear regression models
• MLR
• dissolved oxygen
• DO
• modelling

Acknowledgements

The author thanks the staff of the USGS web server for providing the data that made this research possible.