Prediction of Vistula water surface level by applying the new group method of data handling Online publication date: Sun, 30-Jul-2023
by Amin Mahdavi-Meymand; Wojciech Sulisz
International Journal of Hydrology Science and Technology (IJHST), Vol. 16, No. 2, 2023
Abstract: In this study, novel data-driven models were derived and applied to predict water surface level for two stations of the Vistula river. The applied data-driven models comprise of quadratic polynomial group method of data handling (QP-GMDH), nonlinear group method of data handling (N-GMDH), linear regression equation (LRE), and nonlinear regression equation (NRE). These models were trained by particle swarm optimisation (PSO) and teaching-learning-based optimisation (TLBO) meta-heuristic algorithms. The results confirmed the superiority of GMDHs with respect to the regression equations for big and small datasets. According to the results, the N-GMDH derived in the study is about 4.68% more accurate than the standard GMDH. The TLBO algorithm is about 2.06% more accurate than the popular and widely applied PSO approach. The TLBO method is also more stable in reaching global solutions in comparison with the PSO method.
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