Kargar, K.; Samadianfard, S.; Parsa, J.; Nabipour, N.; Shamshirband, S.; Rabczuk, T. Estimating Longitudinal Dispersion Coefficient in Natural Streams Using Empirical Models and Machine Learning Algorithms. Preprints2019, 2019050110. https://doi.org/10.20944/preprints201905.0110.v1
APA Style
Kargar, K., Samadianfard, S., Parsa, J., Nabipour, N., Shamshirband, S., & Rabczuk, T. (2019). Estimating Longitudinal Dispersion Coefficient in Natural Streams Using Empirical Models and Machine Learning Algorithms. Preprints. https://doi.org/10.20944/preprints201905.0110.v1
Chicago/Turabian Style
Kargar, K., Shahab Shamshirband and Timon Rabczuk. 2019 "Estimating Longitudinal Dispersion Coefficient in Natural Streams Using Empirical Models and Machine Learning Algorithms" Preprints. https://doi.org/10.20944/preprints201905.0110.v1
Abstract
Longitudinal dispersion coefficient (LDC) plays an essential role in modeling the transport of pollution and sediment in the natural rivers. As a result of transportation processes, the concentration of pollution changes along the river. Different studies have been conducted to provide simple equations for estimating LDC. In this study, Support Vector Regression (SVR), Gaussian Process Regression (GPR), M5P and Random Forest (RF) examined to predict the LDC in the natural streams. The hydraulic and geometric features of different rivers gathered for developing the mentioned models for LDC estimation and various statistical criteria were utilized to scrutinize of the models. Furthermore, the Taylor chart was used to evaluate the models and achieved results showed that among machine learning models, M5P displayed the superior performance with CC of 0.823, SI of 0.812, NS of 0.577 and WI of 0.879. As well, S-D model with CC of 0.795 SI of 0.827, NS of 0.558 and WI of 0.890 had more precise results than other empirical models. The results indicates that the developed M5P model with simple formulations was superior to other machine learning models and empirical models and therefore, it can be used as a proper tool for estimating LDC in natural rivers.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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