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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 A review o f criteria in rain water harvesting management(Istanbul Gedik University, 2019) Noori, Shaho; Ghasemlounia, RedvanRainwater harvesting has gained renewed interest in the arid and semi-arid regions since the 1970s. It is important to consider how significant amounts of water can be harvest from a single catchment location. Papers selected address a wide range of rainwater harvesting problems, including the regionalization of nature curves. The rural population has limited income, a high susceptibility to climate change, conventional agricultural activities and adversely affected water shortages. Rain Water Harvest is an alternative cause to water shortages and groundwater depletion issues. The discovery of suitable rainwater harvesting sites is an essential move towards optimizing the amount of water harvesting and mitigating the ecological effect by using remote sensing and GIS techniques. In this article, the main requirements and parameters for selecting appropriate sites for rainwater harvesting have been extract from previous studies.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"(American Society of Civil Engineers (ASCE), 2023) Gharehbaghi, Amin; Ghasemlounia, Redvan...Öğe Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network(Elsevier Science 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 Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region(Springer Science and Business Media Deutschland GmbH, 2025) Gharehbaghi, Amin; Ghasemlounia, Redvan; Vaheddoost, Babak; Ahmadi, FarshadDrought 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.Öğe Evaluation of Drought Using Meteorological Drought Indices, a Case Study: Alanya (Türkiye)(2024) Ghasemlounia, Redvan; Utlu, MustafaDrought is one of the most important challenges that many countries, especially countries in the Middle East region, are struggling with. Based on this, the study and monitoring of hydrological and drought factors is an important issue that can have a significant impact on management decisions in the field of water resources, especially in crisis management. Therefore, investigating the drought parameters is very important to understand the drought situation of a region. In this study, Alanya region, which is located on the southern coast of Turkey, was selected as a case study for drought analysis. Four drought indices for the selected region including: China Z-Index (CZI), Standardized Precipitation Index (SPI), Modified China Z-Index (MCZI) and Z-Score Index (ZSI) have been investigated. All these indicators have been investigated and evaluated using time scales of 1, 6, 12 and 24 months, the coefficient of determination (R2) has been calculated for each drought index with a different time scale and their results have been compared. The findings of the research showed that SPI and CZI drought indices performed better than other selected drought indices in identifying and effectively tracking drought severity. In addition to the study of dry events, wet events were also investigated, which indicates the presence of consecutive floods in the last years of the studied period in the region. The results indicated similar very dry events for the selected indicators in the 6-month period. Also, the rainfall trend for the period of 2015-2022 was taken into consideration to examine the rainfall of the last eight years. The results show that precipitation has decreased in recent years and has a downward trend in most months of the period in question, and the possibility of flood events due to sudden showers in the region has increased due to the continuation of droughts experienced in the years before 2015. Investigating soil moisture and vegetation for the selected period in the study area is also important for the evaluation of the drought level. Evaluation of the available land (vegetation) cover maps of the years 1975, 1985, 2000, 2010, 2020 and 2022 show that the vegetation cover has weakened over the years, and it has been evaluated as an indicator that the danger of drought in the region has increased.Öğe Flood prioritization of basins based on geomorphometric properties using principal component analysis, morphometric analysis and Redvan's priority methods: a case study of hars , it river basin(Elsevier, 2021) Ghasemlounia, Redvan; Utlu, MustafaFlood events in the Hars, it River Basin, which is located in the Eastern Black Sea Region and actively experienced one of the most flood events in Turkey, were discussed based on drainage basin morphometry using three different methods. In this study, 26 sub-basins over 10 km2 are taken into account and flood dynamics of subbasins are evaluated depending on morphometric properties. The results obtained according to the morphometric parameters are evaluated based on statistical techniques, and the flood priority is determined. A total of 20 different indices were used in this study. The obtained morphometry results were evaluated according to the probability of occurrence of floods according to three different methods including morphometric priority, flood priority according to principal component analysis method and Redvan's priority ranking method. Each method is evaluated within itself and flood priorities of sub-basins have been determined. With respect to the 26 subbasins of the Hars, it River and according to the morphometric priority method and results obtained from this method, 8 sub-basins have low flood priority, 12 basins have medium flood priority, and 7 basins have high flood priority. By examining the results obtained from the PCA priority method, 11 sub-basins have low flood priority, 6 sub-basins have medium flood priority, while 9 basins have high flood priority. Finally, according to the Redvan's priority method and its classification, 11 sub-basins are located in the low priority class, 6 sub-basins have medium flood priority. According to this method, the number of sub-basins with high priority is 9. Priorities obtained using three different techniques were checked for accuracy with 6 different statistical parameters based on predicted and inventory datasets. Accordingly, the accuracy value for the RPRM and PCA methods are higher than the MA method. On the other hand, the RPRM method has the highest TN value, which is equal to 7 subbasins, which shows the correct prediction of sub-basins with the low flood risk. Based on the results, it was seen that the obtained values have a high consistency in basin morphometry. The RPRM, which is a new suggested technique in determining basin flood priority, shows that common basins give very similar results with the results obtained according to morphometry and PCA method. Sub-basins with common flood risk, due to outputs of used methods, were compared with recorded floods map of Turkey.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 Improving the thermal comfort of the structures by applying the sustainable engineering requirements(Istanbul Gedik University, 2023) Ali, Basheer Majeed; Ghasemlounia, RedvanModern thermal comfort theories recommend that a restricted temperature difference be evenly maintained throughout all architectural styles, regions, and people. This strategy treats structure inhabitants for heating purposes, resulting in thermal comfort criteria that necessitate power climate management measures. This frequently results in a high need for air conditioning. Initial investigation and new Technology, HVAC (Heating, Ventilation, and Climate Control) advanced facilities are contesting conventional assumptions of thermal comfort criteria on the basis that they ignore main social and environmental comfort elements. In this paper the researcher took the effect of the modification in the design on the thermal comfort by several engineering solutions for reducing the total main radiant temperature (TMRT(, Potential air temperature (PAT), and the developing of the relative humidity (RH) with the wind speed (WS) Which are playing a significant role on improving the urban heat island (UHI) The research mainly aims to analyze the effect of urban green spaces on the urban heat island as a common strategy for improving the thermal comfort.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.Öğe Occupational health and safety practices in small and medium-sized enterprises in the construction sector and a new model proposal(Bursa Technical University, 2024) Altuntaş, Fatih; Ghasemlounia, RedvanThe aim of the conducted study is to create a model that examines the average occupational safety performance level by considering both occupational health and safety performance in the workplace and occupational health and safety management system performance to improve the level of occupational health and safety in small and medium-sized construction companies. For this purpose, 34 small and medium-sized construction companies located in various cities in the Marmara Region constitute the sample group of the study. The data obtained from the sample group were analyzed using factor analysis and descriptive analysis in the SPSS program, and fuzzy logic method in the MATLAB program. With the fuzzy logic approach, two input variables and one output variable were created and defined with five parameters each. Subsequently, with 25 rules created using the fuzzy approach, the calculated average safety index was obtained as 5.69. It is observed that 18 construction companies, constituting 52.8% of the examined 34 small and medium-sized construction companies, have a low safety performance, while 16 construction companies, constituting 47.2%, exhibit high performance.Öğe Tourism impact on coastal erosion: a case of Alanya(Istanbul University, 2023) Razak, Mulhem Abdul; Ghasemlounia, Redvan; Aksel, MuratSandy coasts are constantly exposed to rapid coastal change. Projected climate change caused by Changes in sea level rise, wave circumstances, and storm occurrences will increase erosion rates, exposing these areas to increasingly hazardous conditions. For coastal management purposes, it is important to monitor and measure these changes. Erosion of sandy and pebbly beaches and their ecosystems. The loss of sand and gravel is not only due to the rise in sea level and the force of waves resulting from storms, which will intensify due to climate change. There is a new important factor of human intervention and impact on the beaches that must be mentioned and verified as to how the effect is in the long term with the increase in tourism in the coastal areas, especially in areas of a tourist nature. The amount of sediment that each individual transports from the coastal beaches in the Alanya region. In the experiment, we Collected samples of sand and gravel from different locations on the coast to be surveyed. Classifying the collected samples by means of sieve analysis. Executing the project by going to the sites of sand samples that were analyzed in different periods by collecting samples (collecting sand attached to the bodies of people of different sizes in basins Testing). The thesis also answers Identify the eroded beach by relating the average number of locals and foreigners who come to the project area for a year and use the coast with the data collected during the project.Öğe Uncertainty assessment of kernel based approaches on scour depth modeling in downstream of ski-jump bucket spillways(IWA Publishing, 2021) Ghasemlounia, Redvan; Saghebian, Seyed MahdiFrom the hydraulic structures designer's point of view, the scour depth accurate estimation in downstream of spillways is so important. In this study, the scour depth was assessed downstream of ski-jump bucket spillways using two kernel based approaches namely Support Vector Machine (SVM) and Kernel Extreme Learning Machine (KELM). In the model developing process, two states were tested and the impacts of dimensional and non-dimensional parameters on model efficiency were assessed. The best applied model dependability was investigated via Monte Carlo uncertainty analysis. In addition, the model accuracy was compared with some available semi-theoretical formulas. It was observed that the applied models were more successful than available formulas. The sensitivity analysis results showed that q (unit discharge of spillway) variable in the state 1 and q(2)/[gY(t)(a)] (g is gravity acceleration and Y-t is tail water depth) variable in the state 2 were the most significant parameters in the modeling process. Comparison among applied methods and one other intelligence approach showed that KELM was more successful in predicting process. The obtained results from uncertainty analysis indicated that the KELM model had an allowable degree of uncertainty in the scour depth modeling.Öğe Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs(Taylor & Francis Ltd, 2022) Ghasemlounia, RedvanIn this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (L-jSCVW) is measured via new supplementary experimental datasets via most popular vertex angles theta (128 degrees and 60 degrees). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of L-jSCVW versus the theta, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N-st) in the SCVW, and various tailgate angles (Phi). Via dimensional analysis and pre-processing method, most effectual parameters on the L-jSCVW were obtained as Fr-1 (approaching Froude number), Re-1 (incoming Reynolds number), and y(2)/y(1) (relative sequent depths). Based on the experimental results, by increasing the values of Fr-1 and Re-1, the values of L-jSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the L-jSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the L-jSCVW. According to the attained statistical indices (determination coefficient (R-2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the L-jSCVW with high accuracy.
