Yazar "Haghiabi, AmirHamzeh" seçeneğine göre listele

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    A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs
    (Iwa Publishing, 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan; Afaridegan, Ehsan; Haghiabi, AmirHamzeh; Mandala, Vishwanadham; Azamathulla, Hazi Mohammad; Parsaie, Abbas
    In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%.
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    Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
    (Elsevier, 2024) Parsaie, Abbas; Ghasemlounia, Redvan; Gharehbaghi, Amin; Haghiabi, AmirHamzeh; Chadee, Aaron Anil; Nou, Mohammad Rashki Ghale
    A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science. Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (alpha) for the original MRDRm time series is achieved at 100. Then, the PACF (partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGE') of 0.83, volumetric efficiency (VE) of 0.91, Nash-Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGE' of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s.