Yazar "Gharehbaghi, Amin" seçeneğine göre listele

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    Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region
    (Springer Heidelberg, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Vaheddoost, Babak; Ahmadi, Farshad
    Drought is an intricate natural disaster that substantially menace the world. Its exact forecasting has a remarkable impact in several parts such as food production, business, industry, etc. In this study, in order to assess the drought procedure in Mahabad River basin, the temporal meteorological reconnaissance drought index (RDIMRB) in four diverse time scales including 3, 6, 9, and 12-month are computed using 576 monthly climatic datasets recorded from Sep 1974 to Aug 2022. To predict the time series RDIMRB, different standalone deep neural network (DNN) models including LSTM, GRU, Bi-directional LSTM (Bi-LSTM), and Bi-directional GRU (Bi-GRU) with the sequence-to-one regression module of forecasting (seq2one) are developed. For sake of this aim, the first 70% of data (395 months) and the last 30% of data (169 months) chronologically are used in the calibration and validation parts, respectively, to feed in the models development process. So as to achieve the most advantageous models' structure, a lot of scenarios are adopted by tuning the meant meta-parameters including NHU (number of hidden units), SAF (state activation function), and P-rate (learning dropout rate). According to the performance assessment criteria, total learnable parameters (TLP) criterion, and comparison plots, the Bi-GRU model is verified as the most satisfactory model, and best results are obtained in RDIMRB-12. It under the epitome meant meta-parameters achieved (i.e., NHU = 120, P-rate = 0.5, and softsign as the suitable SAF) results in the RMSE, MBE, NSE, PBIAS, and R2 of 0.17, 0.011, 0.92, -2.02%, and 0.86, respectively, nonetheless for the GRU model are gotten 0.64, 0.071, 0.23, 17.97%, and 0.65, respectively.
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    Parametric and nonparametric regression models in study of the length of hydraulic jump after a multi-segment sharp-crested V-notch weir
    (Iwa Publishing, 2020) Saadatnejadgharahassanlou, Hamid; Zeynali, Rasoul Ilkhanipour; Vaheddoost, Babak; Gharehbaghi, Amin
    A multi-segment sharp-crested V-notch weir (SCVW) was used both theoretically and experimentally in this study to evaluate the length of the hydraulic jump at the downstream of the weir. For this aim, a SCVW with three triangular segments at different tail-water depths (tailgate angles), and ten different discharges at a steady flow condition were investigated. Then, the most effective parameters on the length of the hydraulic jump are defined and several parametric and nonparametric regression models, namely multi-linear regression (MLR), additive non-linear regression (ANLR), multiplicative non-linear regression (MNLR), and generalized regression neural network (GRNN) models are compared with two semi-empirical regression models from the literature. The results indicate that the GRNN model is the best model among the selected models. These results are also linked to the nature of the hydraulic jump and the turbulent behavior of the phenomenon, which masks the experimental results with outliers.
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    Three dimensional flow simulation over a sharp-crested V-Notch weir
    (Elsevier Sci Ltd, 2020) Saadatnejadgharahassanlou, Hamid; Zeynali, Rasoul Ilkhanipour; Gharehbaghi, Amin; Mehdizadeh, Saeid; Vaheddoost, Babak
    Thin-plate weirs are widely used to monitor the flow rate in open channels. Thereby, three dimensional (3D) modeling of the flow over a weir in an open channel can be considered as one of the main topics in hydraulic science. In this study, the flow over a sharp-crested v-notch weir (SCVW) is simulated by a 3D numerical model. Laboratory experiments were conducted to monitor and measure the behavior of the SCVW in practice. Finally, the simulated velocity distributions, water surface profiles, and hydraulic jump were compared with those of the experimental data. Due to the turbulent nature of the flow over the SCVW, a Reynolds stress model (RSM) and three types of the k-e turbulence models with the fractional volume of fluid technique (VOF) were used in the analysis. In this respect, the two-phase solution method and dense mesh were used in generating the simulation domain. Results indicated that the RSM exhibited higher accuracy in defining the velocity distribution, complex flow pattern, and predicting the hydraulic jump formation downstream of the SCVW.