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Öğe Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers(Mdpi, 2025) Achite, Mohammed; Katipoglu, Okan Mert; Kartal, Veysi; Sarigol, Metin; Jehanzaib, Muhammad; Gul, EnesThe rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network-recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: -0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: -0.018, and R: 0.597.Öğe Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models(Springer Int Publ Ag, 2025) Saroughi, Mohsen; Katipoglu, Okan Mert; Akturk, Gaye; Gul, Enes; Simsek, Oguz; Citakoglu, HaticeArtificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.Öğe Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiye(Mdpi, 2023) Gul, Enes; Staiou, Efthymia; Safari, Mir Jafar Sadegh; Vaheddoost, BabakThe impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Turkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R-2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Turkiye.Öğe Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach(Irtces, 2023) Gul, Enes; Safari, Mir Jafar Sadegh; Dursun, Omer Faruk; Tayfur, GokmenUncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELMPSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUNbased hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. (c) 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.Öğe Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms(Iwa Publishing, 2021) Gul, Enes; Dursun, O. Faruk; Mohammadian, AbdolmajidHydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly expanding stilling basin were estimated using hybrid Extreme Learning Machine (ELM). To hybridize ELM, Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) metaheuristic algorithms were implemented. In addition, six different models were established to determine effective dimensionless (relative) input variables. A new dataset was constructed by adding the data obtained from the experimental study in the present research to the data obtained from the literature. The performance of each model was evaluated using k-fold cross validation. Results showed that ICA hybridization slightly outperformed FA and PSO methods. Considering relative input parameters, Froude number (Fr), expansion ratio (B) and relative sill height (S), and effective input combinations were Fr - B- S and Fr - B for the prediction of the sequent depth ratio (Y) and relative hydraulic jump length (L-j/h(1)), respectively.Öğe Experimentally Verified Numerical Investigation of the Sill Hydraulics for Abruptly Expanding Stilling Basin(Springer Heidelberg, 2023) Aydogdu, Mahmut; Gul, Enes; Dursun, Omerul FarukEnergy dissipation structures, particularly stilling basins, are critical for defining the hydraulic jump characteristics that are suitable. Appropriate sill geometry for abruptly expanding stilling basins has been investigated and a central rectangular sill has been proposed in the literature. This study has examined the suggested central sill and alternative flip buckets for abruptly expanding stilling basins. A series of experimental and numerical studies were carried out for two different heights of the central sill and two different flip buckets. Simulations have been evaluated using experimental data of laboratory scale, which indicated that they were acceptably precise. For the simulations, the k-epsilon turbulence model RNG module was preferred using the volume of fluid methods. The PISO approach was chosen to resolve this equation system numerically. The results showed that the hydraulic jump characteristics are strongly influenced by sill geometry. For the Type-3 sill negative static pressures have not occurred and performs better at energy dissipation than other geometries examined in the study. Higher pressures occurred on the rectangular prism-shaped sills. Maximum static pressure happened on the Type-2 sill. The least static pressure was seen in the Type-4 sill type.Öğe Exploring the attributive impacts of climate and land use changes on streamflow in an urbanized watershed by deep learning and physically based modeling(Elsevier Sci Ltd, 2025) Geykli, Abi Nazari; Gul, Enes; Naderi, Somayeh; Rasouli, Kabir; Nikoo, Mohammad RezaWater security in urban watersheds is increasingly becoming challenging as climate and land use changes intensify the hydrological cycle, shifting streamflow patterns and causing uncertainties in availability of water resources. In this research, using the advantages of both deep learning and physically based modeling, we apply the Soil and Water Assessment Tool (SWAT) coupled with Long Short Term Memory (LSTM) or LSTM-Attention to explore the relative attribution of changes in streamflow to land use change (LUC) and climate change in the Village Creek Watershed in Alabama, USA. SWAT was used to represent the physical processes of infiltration, sediment transport, and evapotranspiration, and the deep learning models were used to establish a connection between streamflow and statistically downscaled rainfall and temperature time series under present and future climates. The future precipitation levels for the period 2043-2055 are expected to rise by as much as 21 %, and minimum temperatures will decrease by-0.33 degrees C during winter seasons in the study area, leading to increased frequency of flooding and changed water resource availability. Land use change practice created an unexpected effect of streamflow reduction, by-6 % during colder months, in the study basin even though the effect was less compared to other processes. When changes in climate and land use are combined in simulation models, their combined effects, an increase of 12 %, drive more extreme changes in streamflow than from their individual impacts, +6 % for climate and-1 % for land use. The results showed the necessity for integrated management strategies because of complex non-linear responses to concurrent environmental changes. Comprehensive assessment across multiple gauging stations and performance metrics confirm the reliability of these models in representing both baseflow and high-flow conditions. In particular, the performance of Kling-Gupta efficiency (KGE) for LSTM-Attention remained above 0.90. Furthermore, the synergy between physically explicit and deep learning models uncovers emergent hydrological patterns, reinforcing the necessity of anticipatory planning for climate resilience.Öğe Flood hazard mapping using M5 tree algorithms and logistic regression; a case study in East Black Sea Region(Springer Heidelberg, 2023) Yukseler, Ufuk; Toprak, Ahmet; Gul, Enes; Dursun, O. FarukFlood is a type of disaster that occurs as a result of the overflow of the stream outside its bed. Similarly to many parts of the globe, particularly the Eastern Black Sea Region of Turkey is frequently exposed to major floods. The heavy rainfall and topographic structure of the region and the proximity of settlements to stream beds are the primary causes of flooding. The present study pertains to the utilization of the Logistic Regression (LR), M5P Rule Tree (M5PRT) and M5P Regression Tree (M5PRGT) models for the assessment of the flood hazard areas in and around the Of district, located on the Black Sea coast of Trabzon province. According to flood inventory, 16 flood events occurred in 5 different locations in the study area. These areas were converted into point data, and comprising a total of 1600 points, 800 flooded and 800 non-flooded, were determined by random sampling. Accordingly, flood hazard maps were created with 8 flood parameters and 3 different methods. Accuracies of these models were evaluated through AUC (Receiver Operating Characteristics Curve), ACC (Accuracy), R (Recall), P (Precision) and F (F-Score). Analyses showed that the Tree-Based Algorithms are more successful than the LR method in detecting the flood hazards. In addition, the altitude and precipitation were found out to be the most influential parameters in all 3 methods on the occurrence of flooding events in the region. The confluence points of the streams, the coastal plain where the stream disembogues to the sea and the valley floors in and around the Of district were designated as the areas with high risk of flooding.Öğe Functional Evaluation of Coronary Stenosis: is Quantitative Flow Ratio a Step Forward?(Czech Soc Cardiology & Czech Soc Cardiovascular Surgery, 2022) Askin, Lutfu; Tanriverdi, Okan; Gul, EnesThe severity of coronary lesions (CL) has always been an important aspect of patient care during coronary angiography. Fractional flow reserve (FFR)-guided coronary treatments have been shown to have a positive influence on clinical outcomes. However, significant technical flaws in clinical practice have slowed global adoption of FFR. Other indices, such as the quantitative flow ratio (QFR), have recently been created and tes-ted in clinical research. According to a computational fluid dynamics study, QFR has a good correlation with FFR values and saves time and money compared to other methods. QFR is a new angiographic technique that uses modern software to reconstruct three-dimensional vessels and calculate flow models. Modern, effective and usable tool for CL due to significant technical benefits. We aimed to analyze the application areas of the QFR and its potential clinical application in this review.Öğe Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport(Mary Ann Liebert, Inc, 2023) Gul, Enes; Safari, Mir Jafar SadeghSediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.Öğe An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria(Pergamon-Elsevier Science Ltd, 2023) Achite, Mohammed; Gul, Enes; Elshaboury, Nehal; Jehanzaib, Muhammad; Mohammadi, Babak; Mehr, Ali DanandehDrought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-fuzzy inference system (ANFIS) optimized by the water cycle algorithm (WCA), for hydrological drought forecasting in semi-arid regions of Algeria. The new model was used to predict SRI at 3-, 6-, 9-, and 12 -month accumulation periods in the Wadi Mina basin, Algeria. The results of the model were assessed using four criteria; determination coefficient, mean absolute error, variance accounted for, and root mean square error, and compared with those of the standalone ANFIS model. The findings suggested that throughout the testing phase at all the sub-basins, the proposed hybrid model outperformed the conventional model for estimating drought. This study indicated that the WCA algorithm enhanced the ANFIS model's drought forecasting accuracy. The pro-posed model could be employed for forecasting drought at multi-timescales, deciding on remedial strategies for dealing with drought at study stations, and aiding in sustainable water resources management.Öğe Investigation of the effect of variable-sized energy dissipating blocks on sluice gate performance(Water Research Commission, 2024) Gul, Enes; Kilic, Zeyneb; Ikinciogullari, Erdinc; Aydin, M. CihanThe present research used a combination of experimental and numerical methods to investigate energy dissipation blocks of different heights placed downstream of a sluice gate in an open channel flow. Numerical model simulations were performed using a 3D computational fluid dynamics (CFD) technique, using the Reynolds-averaged Navier-Stokes (RANS) equations with the volume of fluid (VOF) and k-epsilon turbulence models. The accuracy of the numerical model and the grid sensitivity was assessed according to a recommended procedure in the literature. Different hydraulic and geometry conditions were investigated to understand the energy dissipation behaviour of the blocks. The hydrodynamic effects of different block spacings, heights and configurations were analysed by means of CFD simulations. The results show that the variable size blocks have a high energy dissipation efficiency in sluice gate flows, particularly at high Froude numbers. The energy dissipation efficiency of the blocks downstream of a sluice gate can reach up to 55% for high discharges (Q = 35 L/s). Interestingly, the energy dissipation performance of small gate openings exceeds that of large gate openings, reaching a peak efficiency of 40% for the same discharge. In addition, the block spacing has a minimal effect on the energy dissipation, while smaller block spacing results in a smoother water surface profile.Öğe Investigation of the flow characteristics of slit check dams using novel models(Springer Heidelberg, 2024) Emiroglu, Muhammet Emin; Ikinciogullari, Erdinc; Yalcin, Eyyup Ensar; Gul, EnesFloods, which cause loss of life and property and destruction of the environment, have devastating effects on socio-economic welfare. Slit-check dams are essential structures for managing the transport of silt and woody debris, especially in events of significant floods. The current study presents the hydraulic characteristics of slit-check dams with different geometries for experimental and numerical tests. First, the Butterfly model was produced with a 3D printer and examined experimentally. Then, the Butterfly model was validated extensively using OpenFOAM (v7) software for the numerical analysis. Finally, the other models were examined numerically using the k-epsilon turbulence model. The changes in water surface profile, velocity profiles, energy dissipation rates, and streamlines were comprehensively examined and discussed. The results showed that slit-check dams caused hydraulic jumps and dissipated flow energy. The Arced and Rectangular models, in particular, demonstrated a significant performance for energy dissipation, which is essential for flood management. Water surface profiles are directly affected by discharge. Moreover, the cross-sectional length of the model in question significantly affects the water surface profile. Accordingly, an increase was observed in the velocity profiles along the slit-check dam. While the maximum velocity for all unit discharge was observed in the V-shaped model, the minimum velocities were observed for the Arced and Rectangular models. Thus, the energy absorption performance of Arced and Rectangular models is higher.Öğe Local scour protection using geocell for downstream of spillway(Academic Publication Council, 2021) Gul, Enes; Sarici, Talha; Dursun, Omerul FarukLocal scour is an important problem for hydraulic structures. The local scour in the downstream of dams causes problems such as the damage of the dam body stabilization, erosion of the slopes, and the submergence of the turbines. There are many studies investigating the local scour prediction of the downstream of the hydraulic structures, but in recent years, these studies have been replaced by studies of local scour reduction. The new idea of confining the bed materials using the geocell is becoming a popular solution. This solution can be especially used for the reinforcement of the soils. In this study, the preventability of the local scour downstream of chute channel by cellular confinement system, also known as geocell, was investigated. As a result, in case of using geocell, percentage reduction of the maximum scour depth up to 40.63% was observed.Öğe Modeling uniaxial compressive strength of some rocks from turkey using soft computing techniques(Elsevier Sci Ltd, 2021) Gul, Enes; Ozdemir, Engin; Sarici, Didem ErenUniaxial compressive strength (UCS) is substantially used mechanical parameters to observe and classification of rocks, but this test is subsersive, taking a long time and required well equipped laboratory conditions and properly prepared samples. Therefore it is important to estimate this parameter from other physico-mechanical rock parameters that are nondestructive, easy to prepare samples and required less time. Machine learning methods which are among these methods and increase their importance and validty are Multilayer Perceptron Neural Network (MLPNN), M5 Model Tree (M5MT), Extreme Learning Machine (ELM) methods. In this study, Brazilian tensile strength, ultrasonic P-wave velocity, shore hardness tests of different rock types (Basalt, limestone, dolostone) were performed. The results were used for estimating UCS using MLPNN, M5MT, ELM methods. The validation of models were checked root mean squared error (RMSE), mean absolute error (MAE), variance account for (VAF) and coefficient of determination (R-2) and a10-index. Weights and bias values for MLPNN and ELM approaches and the tree structure for the M5MT method are presented. The result indicated MLPNN model outperforms the other models. Based on the result of predictive models with RMSE, MAE, VAF and R-2 equal to RMSE: 1.3421, MAE: 0.7985, VAF: 99.7409, R-2: 0.9982%.Öğe A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM(Elsevier Sci Ltd, 2022) Cemalgil, Selim; Gul, Enes; Onat, Onur; Aruntas, Hilseyin YilmazThe service life performance of conventional and modified concrete subjected to harsh climatic condition environment is directly related to durability properties of concrete like abrasion, freezing and thawing cycles. These properties are critical issues that should be predicted before performing experimental test. On this basis, the basic purpose of this paper is to predict the abrasion loss, freezing and thawing properties of concrete modified with silica fume (SF) and steel fiber (SFb) by using mix design and additional properties. From this point of view, a conducted experimental study was selected as a case study. In the control concrete (CC) mixtures, Portland cement, crushed stone aggregate, and superplasticizer (SP) were used in the selected experimental study. SP in concrete mixtures was used in the amounts of 1.0%, 1.5%, and 2.0% by weight of cement, and so modified concrete was produced with and without SFb according to the target strength of C25. Furthermore, SF and SFb were used in different amounts to modify the concrete. The SF was replaced with cement in the amounts of 7.5%, 10.0%, and 15.0%. In total, 16 different mix designs were prepared with different SP and SF ratios. In addition, SFb was added to all mixtures of designed concrete at a constant amount of 65 kg/m3. Additionally, a 16-mix design was prepared with SFb. Cumulatively, 32 different mix designs were prepared for the experimental study. Tests on the fresh, hardened, and life-cycle performance properties of the concrete were conducted. As for the metaheuristic part of this study, on the basis of the available experimental data, life-cycle performance parameters of the concrete modified with SF and SFb are predicted by using single and hybrid generalized extreme learning machine methods. Eight different data sets were generated with gradually extended input data. Two different outputs were considered: abrasion resistance (AL) and freezing/thawing (FT). Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms were used to produce binary and ternary hybrid methods. Four different models were proposed as listed: single use of Generalized Extreme Learning Machine (GRELM), binary use of GRELM-PSO, and GRELM-GWO. Finally, PSO and GWO were hybridized and integrated into GRELM. Two quality indicators, namely Root Mean Square Error (RMSE) and correlation of determination (R2), were considered to see the performance of the prediction. The results showed that the proposed ternary prediction model composed of GRELM-PSO-GWO provided more accurate results in all sets from 74% to 91% by extending input parameters, even if complicated parameters are inserted in as an input to the data set.Öğe Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes(Springer Heidelberg, 2023) Kouzehkalani Sales, Ali; Gul, Enes; Safari, Mir Jafar SadeghSediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (t(s)) or deposited bed width (W-b) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t(s) and W-b can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W-b/Y, t(s)/Y, W-b/D, and t(s)/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W-b/Y, t(s)/Y, W/D, and t(s)/D are considered for model development. It is found that models that incorporate sediment bed thickness (t(s)) provide better results than those which use deposited bed width (W-b) in their structures. Among four different scenarios, models that utilized t(s)/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.Öğe Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging(Mdpi, 2023) Mehr, Ali Danandeh; Tur, Rifat; Alee, Mohammed Mustafa; Gul, Enes; Nourani, Vahid; Shoaei, Shahrokh; Mohammadi, BabakMachine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.Öğe Regression models for sediment transport in tropical rivers(Springer Heidelberg, 2021) Harun, Mohd Afiq; Safari, Mir Jafar Sadegh; Gul, Enes; Ab Ghani, AminuddinThe investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.Öğe Robust optimization of SVM hyper-parameters for spillway type selection(Elsevier, 2021) Gul, Enes; Alpaslan, Nuh; Emiroglu, M. EminSpillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited. The tuning of the hyperparameters in machine learning algorithms is still an open problem. In this paper, a parallel global optimization algorithm is proposed optimizing the hyper-parameters of a Support Vector Machine (SVM) classification model for providing accurate spillway type selection (STS). The random forest method is used to obtain the relative importance of input variables. Besides, a novel spillway dataset was introduced and a novel STS software tool has been developed based on different machine learning algorithms. Several experiments are carried out to demonstrate the effectiveness of the proposed tool and the reliability of data. The hyper-parameters optimized SVM was achieved the best results with 93.81% classification accuracy. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.
