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Öğe 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, AbbasIn 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%.Öğe Application of AI approaches to estimate discharge coefficient of novel kind of sharp-crested v-notch weirs(American Society of Civil Engineers (ASCE), 2022) Gharehbaghi, Amin; Ghasemlounia, RedvanIn this study, the hydraulic features of the SCVW (a novel type of sharp-crested V-notch weirs) were scrutinized in the popular vertex angles theta, i.e., 30 degrees, 45 degrees, 60 degrees, 90 degrees, 120 degrees, 128 degrees, and 150 degrees, under aerated, steady and free overflow conditions in an open channel for large physical models. To assess the changes of the discharge coefficient of the SCVW (i.e., C-dSCVW) versus weir height and theta, widespread laboratory works were performed by measuring the water head over the crest of the weir and the discharge. Special formulas for the C-dSCVW in the theta=60 degrees were checked, and an appropriate empirical equation was recommended. The calculated C-dSCVW by the proposed equation was within 0%-10% of the measured values. Three types of nonparametric artificial intelligence (AI) methods, namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) were developed to estimate the C-dSCVW. For the sake of modeling, 196 experimental datasets were applied in the mentioned methods to evaluate the C-dSCVW by taking into consideration the dimensionless variables which impact the determining procedure of the C-dSCVW. In this modeling, by varying the architecture and core factors of the aforementioned methods, several scenarios were defined. The generated mathematical equation of C-dSCVW by the best scenario of the GEP was compared with the corresponding measured values in which the results were in 0%-10%. According to the attained statistical indices, scatter plots, and the values of the total grade (TG) technique, the hybrid SVR(RBF)-ACO model was determined as the superior and optimal method to estimate the C-dSCVW with high performance and accuracy.Öğe Application of data-driven models to predict the dimensions of flow separation zone(Springer Heidelberg, 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan; Latif, Sarmad Dashti; Haghiabi, Amir Hamzeh; Parsaie, AbbasIn this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes' shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55 degrees. In this direction, a butterfly's array for the vanes' arrangement along with different main controlling factors such as distances of vanes along the flow (delta(l)), degree of curvature (beta), and angles of attack to the local primary flow direction (theta) is utilized. Through capturing photos and utilizing AutoCAD and SURFER software, maximum relative length and width are calculated. Based on the experimental measurements, maximum percentage reduction of DFSZ, in comparison with the controlled test (without submerged vanes), is obtained with theta =30 degrees, beta = 34 degrees, and delta(l) = 10 cm with value of 78 and 76%, respectively. Moreover, several data-driven models, namely, gene expression programming (GEP), support vector regression (SVR), and a robust hybrid SVR with an ant colony optimization algorithm (ACO) (i.e., hybrid SVR-ACO model), are developed in order to predict DFSZ via the operative dimensionless variables realized by Spearman's rho and Pearson's coefficient processes. In accordance with the statistical metrics, model grading process, scatter plot, and the hybrid SVR(RBF)-ACO model are preferred as the best and most precise model to predict maximum relative length and width with a total grade (TG) of 6.75 and 5.8, respectively. The generated algebraic formula for DFSZ under the optimal scenario of GEP is equated with the corresponding measured ones and the results are within 0-10%.Öğe Closure to Application of AI Approaches to Estimate Discharge Coefficient of Novel Kind of Sharp-Crested V-Notch Weirs(Asce-Amer Soc Civil Engineers, 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan[Abstract Not Available]Öğe Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network(Elsevier Sci Ltd, 2021) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Saadatnejadgharahassanlou, HamidIn this study, mean monthly groundwater level (GWL) fluctuations of four different monitoring piezometers are forecasted in an agricultural area by developing new proposed structures of BiLSTM based neural network models. For developing models, 186 monthly measured water table depth during time period (Sep 2001-Feb 2017) are employed. Due to a lack of meteorological variables records, the module of sequence-to-one is employed. By tuning several hyperparameters such as the number of hidden units (NHU), kind of state activation function (SAF), learning dropout rate (P-rate), and network topology pattern (NTP), the performance of designed models is improved. Based on results of modelling, in all designed models, the optimal P-rate is obtained 0.5, and the running time decrease by increasing P-rate in the same model. Since harsh fluctuations of GWL, the general Single-LSTM mode (1) and Single-BiLSTM model (2) perform too weak compared to other models. Nonetheless, by deepening models through adding the suitable hidden layers instead of many numbers of nodes, the performance of Single-BiLSTM model is improved strikingly. Based on statistical evaluation metrics, violin plot, and also TLP (Total Learnable Parameters) value, novel proposed model (5) (simple Double-BiLSTM model combined by a Multiplication layer (x)) is selected as the superior model. In the piezometer 4 with a range of 4.49 (m), the model (5) yielded in an R-2 of 0.89 and an RMSE of 0.17 (m), while in the same physical characteristics, the simple Double-BiLSTM model (3) yielded in an R-2 of 0.77 and an RMSE of 0.25 (m).Öğe Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature(Springer Science and Business Media B.V., 2024) Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Albaji, MohammadThe precise predicting of air temperature has a significant influence in many sectors such as agriculture, industry, modeling environmental processes. In this work, to predict the mean daily time series air temperature in Mu & gbreve;la city (AT(m)), Turkey, initially, five different layer structures of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning-based neural network models through the seq2seq regression forecast module are developed. Then, based on performance evaluation metrics, an optimal DL-based layer network structure designed is chosen to hybridize with the wavelet transform (WT) algorithm (i.e., WT-DNN model) to enhance the estimation capability. In this direction, among potential meteorological variables considered, the average daily sunshine duration (SSD) (hours), total global solar radiation (TGSR) (kw. hour/m(2)), and total global insolation intensity (TGSI) (watt/m(2)) from Jan 2014 to Dec 2019 are picked as the most effective input variables through correlation analysis to predict AT(m). To thwart overfitting and underfitting problems, different algorithm tuning along with trial-and-error procedures through diverse types of hyper-parameters are performed. Consistent with the performance evaluation standards, comparison plots, and Total Learnable Parameters (TLP) value, the state-of-the-art and unique proposed hybrid WT-(LSTM x GRU) model (i.e., hybrid WT with the coupled version of LSTM and GRU models via Multiplication layer (x)) is confirmed as the best model developed. This hybrid model under the ideal hyper-parameters resulted in an R-2 = 0.94, an RMSE = 0.56 (degrees C), an MBE = -0.5 (degrees C), AICc = -382.01, and a running time of 376 (s) in 2000 iterations. Nonetheless, the standard single LSTM layer network model as benchmark model resulted in an R-2 = 0.63, an RMSE = 4.69 (degrees C), an MBE = -0.89 (degrees C), AICc = 1021.8, and a running time of 186 (s) in 2000 iterations.Öğe Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks(Elsevier, 2022) Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Albaji, MohammadPrecise estimation of groundwater level (GWL) fluctuations has a substantial effect on water resources management. In the present study, to forecast the regional mean monthly time series groundwater level (GWL) with a range of 4.82 (m) in Urmia plain, three different layer structures of Gated Recurrent Unit (GRU) deep learningbased neural network models via the module of sequence-to-sequence regression are designed. In this sense, 180time series datasets of regional mean monthly meteorological, hydrological, and observed water table depths of 42 different monitoring piezometers during the period of Oct 2002-Sep 2017 are employed as the input variables. By using Shannon entropy method, the most influential parameters on GWL are determined as regional mean monthly air temperature (Tam), precipitation (Pm), total (sum) water diversion discharge (Wdm) of four main rivers. Nevertheless, Cosine amplitude sensitivity analysis confirmed Tam as a dominant factor. For preventing overfitting problem, an algorithm tuning technique via different kinds of hyperparameters is operated. In this respect, several scenarios are implemented and the optimal hyperparameters are accomplished via the trialand-error process. As stated by the performance evaluation metrics, Model Grading process, and Total Learnable Parameters (TLP) value, the innovative and unique suggested model (3), entitled GRU2+, (Double-GRU model coupled with Addition layer (+)) with seven layers is carefully chosen as the best model. The unique suggested model (3) in the optimal hyperparameters, resulted in an R2 of 0.91, a total grade (TG) of 7.76, an RMSE of 0.094 (m), and a running time of 47 (s). Thus, the model (3) can be certainly employed as an effective model to forecast GWL in different agricultural areas.Öğe Machine learning approaches coupled with variational mode decomposition: a novel method for forecasting monthly reservoir inflows(Springer Heidelberg, 2024) Ahmadi, Farshad; Ghasemlounia, Redvan; Gharehbaghi, AminReliable and precise reservoir inflow predicting is very significant for water resource management. In this research, different single and hybrid Variational Mode Decomposition (VMD) with estimation models including K-star (K*), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) models are developed to predict long-term time series of average monthly reservoir inflows in Baroon Dam (RIBDm) sited in Maku city, Northwest Iran. Using Pearson's correlation coefficient (PCC) analysis among observed potential meteorological predictors and RIBDm confirms the rainfall (Pave) as the only effective input variable. To reduce the influence of overfitting problems and well-configuration of the approaches developed, an algorithm tuning over meta-parameters together with a trial-and-error technique are applied. The outcomes of modeling show that in the both single K* and hybrid VMD-K* models, the optimum value of the global blend parameter (b) is 10%, yet, by rising the value of b from 10 to 100%, the accuracy of both models are markedly reduced. In both standard LSTM and hybrid VMD-LSTM models, the ideal dropout rate (P-rate) is gained 0.5. Likewise, in both models, as number of hidden neurons (NHN) is held constant, increasing P-rate causes to decrease running time, also as P-rate remains constant, increasing NHN causes to increase running time. Results of statistical indicators and visual analysis of comparison plots approve the hybrid VMD-LSTM model as the superior method with an R2 of 0.8, KGE of 0.87, RMSE of 1.15 (m3/s), and MBE of 0.15 (m3/s). Nonetheless, under the ideal scenario by the K* model, R2 is 0.27, RMSE is 2.75 (m3/s), KGE is 0.43, and MBE is 0.15 (m3/s).Öğe 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 GhaleA 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.
