Simulating Caspian Sea surface water level by artificial neural network and support vector machine models

Acta Geophysica

article

Khaledian, M. R.¹  Isazadeh, M.³  Biazar, S. M.³  Pham, Q. B.⁴ 

[1] Water Engineering Department, University of Guilan;Department of Water Engineering and Environment, Caspian Sea Basin Research Center;Department of Water Engineering, University of Tabriz;Department of Hydraulic and Ocean Engineering, National Cheng-Kung University

关键词: Anzali;    Kernel function;    Uncertainty;   

DOI  :  10.1007/s11600-020-00419-y

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

Reduction in sea water level can make services in nearshore structures difficult, and sea water level rise increases the risk to residential areas or the surrounding fields. For strategic planning, it is vital to take into account the present and future fluctuations of Caspian Sea water level. In this study, support vector machine and artificial neural network are used to estimate water level of the Caspian Sea. A 34-year period dataset is used as input data for water level on the scale based at Anzali, Iran. Performances of these two models are compared according to some statistical indices. Results of this study indicate that support vector machine with an error of 4.782 mm and r = 0.96 simulated the time series better, as compared with artificial neural network with an error of 5.014 mm and r = 0.957; furthermore, the uncertainty of this model is lower than that of the artificial neural network, i.e., 0.04 verses 0.22.

【 授权许可】

Unknown