A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River

Geoscience Frontiers

Hamid Zare Abyaneh¹  Ali Danandeh Mehr²  Ehsan Olyaie³ 

[1] Department of Water Engineering, College of Agriculture, Bu-Ali Sina University, Hamedan, Iran;Istanbul Technical University, Civil Engineering Department, Hydraulics Division, 34469 Maslak, Istanbul, Turkey;Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran;

关键词: Dissolved oxygen;    SVM;    LGP;    ANN;    Modeling;   

DOI  :  10.1016/j.gsf.2016.04.007

来源: DOAJ

【 摘 要 】

Most of the water quality models previously developed and used in dissolved oxygen (DO) prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising: (1) two types of artificial neural networks (ANN) namely multi linear perceptron (MLP) and radial based function (RBF); (2) an advancement of genetic programming namely linear genetic programming (LGP); and (3) a support vector machine (SVM) technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), mean absolute relative error (MARE) and, correlation coefficient statistics (R) were used to choose the best predictive model. The comparison of estimation accuracies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.

【 授权许可】

Unknown