Showing 9 results for Support Vector Machine
M. Mokhtari, A. Najafi,
Volume 19, Issue 72 (8-2015)
Abstract
Land use classification and mapping mostly use remotely sensed data. During the past decades, several advanced classification methods such as neural network and support vector machine (SVM) have been developed. In the present study, Landsat TM images with 30m spatial resolution were used to classify land uses through two classification methods including support vector machine and neural network. The results showed that SVM and neural network with the total accuracy of 90.67 % and 91.67% are superior. SVM had a better performance in separating classes with similar spectral profiles. In addition, SVM showed a better performance in delineating class borders in comparison with neural network method. In summary, both SVM and neural network showed satisfactory results but the method of support vector machine proved better with a difference of 1% and 2% in overall accuracy and kappa coefficient, respectively. This was an expected outcome because SVMs are designed to locate an optimal separating hyperplane, while ANNs may not be able to locate this separating hyperplane.
K. Roshangar, R. Valizadeh,
Volume 21, Issue 2 (8-2017)
Abstract
Hydraulic jump is the most common method of dissipating water’s kinetic energy in downstream of spillways, shoots and valve. In this paper, Support Vector Machine (SVM) method, as a machine learning method, have been used to estimate hydraulic characteristics such as the sequent depth ratio, jump length and energy loss in three different sudden expansions stilling basins, and the rate of influence of input parameters in each jump has been analyzed. In order to evaluate the performance of proposed method, 936 sets of the observed data have been used for training and testing process of three kinds of expanding channel models. Furthermore, a comparison between semi-theoretical approaches and the data obtained from the best SVM models have been carried out. The results confirmed the efficiency of SVM method for estimating the hydraulic jump characteristics and proved that this method performed well in comparison to the semi-theoretical relationships. The obtained results revealed that the superior model for the sequent depth ratio and relative energy dissipation was the model with (Fr1,h1/B) parameters and the superior model for the length of hydraulic jump prediction was the model with (Fr1, h2/h1) parameters.
M. Majedi Asl, M. Fuladipanah,
Volume 22, Issue 4 (3-2019)
Abstract
A labyrinth weir is a nonlinear weir folded in the plan-view which increases the crest length and the flow rate for a given channel width and an upstream flow depth. Nowadays, a labyrinth weir is an attractive alternative for those weirs that have a problem in passing the probable maximum flood. The three-dimensional flow pattern and unlimited geometric parameters provide a major challenge to the designers of these weirs. The present study aimed at determining discharge coefficients of sharp-crested triangular labyrinth weirs using the support vector machine (SVM). The results were compared with the experimental data. For this purpose, 123 laboratory test data including geometric and hydraulic parameters such as vertex angle (θ), magnification ratio (L/B), head water ratio (h/w), Froude number (Fr), Weber Number (We) and Reynolds number (Re) were used. The results showed that the SVM-based model produced the most accurate results when only three geometric parameters, e.g. (h/w, θ, L/B), were introduced as the input parameters (R2 = 0.974, Root mean square error [RMSE] = 0.0118, mean absolute error [MAE] =0.0112 and mean normal error [MNE] =0.017 for the test stage). Also, for these weirs, polynomials linear and nonlinear regression equations were presented. Finally, the discharge coefficient of sharp-crested triangular labyrinth weirs based on the Rehbock equation was evaluated and compared with the SVM using nonlinear and linear regression methods.
E. Shrifi Garmdareh, M. Vafakhah, S. Eslamian,
Volume 23, Issue 1 (6-2019)
Abstract
Flood discharge estimation with different return periods is one of important factors for water structures design and installation. On the other hand, a lot of rivers existing in Iran watersheds have no complete and accurate hydrometric data. In these cases, one of the suitable solutions to estimate peak discharges with different return periods is the regional flood analysis. In this research, 55 hydrometric stations were used. For this purpose, at first, peak discharges in different return periods were estimated using the EasyFit software. Then, the effective variables on the peak discharges were collected and the input variables of the models were selected by using gamma test with the help of the WinGamma software. Finally, data modeling was performed using the support vector machine, artificial neural networks and nonlinear multivariate regression techniques. Quantitative and qualitative assessment of the results using various indices including Nash-Sutcliffe Efficiency Coefficient (NSC) showed that SVM modeling method had the most accuracy in comparison to the other two modeling methods to predict the peak discharges in the Namak Lake Watershed.
M. Majedi Asl, S. Valizadeh,
Volume 23, Issue 4 (12-2019)
Abstract
Local scour around the foundation of marine and hydraulic structures is one of the most important factors in the instability and destruction of these structures. False prediction of scour depth around bridges has caused financial losses in plasticization and endangered many people's lives. Therefore, an accurate estimation of this complex phenomenon around the bridges is necessary. Also, since the formulas presented by different researchers relate to laboratory conditions, they are less true and less accurate in other situations. Recently, many researchers have tried to introduce new methods and models called soft calculations in predicting this phenomenon. In this research, 146 different laboratory data series (three different laboratory conditions) were analyzed using a backup vector machine to predict scour depth around the bridge head. These data are presented in the form of various combinations of input parameters which, respectively, represent thickness under the slippery layer, Reynolds number, critical velocity, Shields parameter, velocity Shear, average speed, flow depth, the average diameter of the particles and diameter of the bridge. The parameters in two different scenarios (the mode with dimension and mode) were introduced into the SVM network and the results of this machine were compared with those obtained from the experimental formulas and relations presented in this study. The results showed that in the first scenario, the combination of No. 5 with input parameters () and in the second scenario, the combination No. 5 with input parameters () for the test stage were selected as the best model. It was also concluded from the results that the scenario two (the state with dimension) in predicting the scour depth around the vertical single-pillar provided a more accurate estimate than the first scenario (barrier state). At the end, the sensitivity analysis was carried out on the parameters and the parameters D, U*, V were selected, respectively, as the most effective parameters
S. H. Roshun, K. Shahedi, M. Habibnejad Roshan, J. Chormanski,
Volume 25, Issue 2 (9-2021)
Abstract
The simulation of the rainfall-runoff process in the watershed has particular importance for a better understanding of hydrologic issues, water resources management, river engineering, flood control structures, and flood storage. In this study, to simulate the rainfall-runoff process, rainfall and discharge data were used in the period 1997-2017. After data qualitative control, rainfall, and discharge delays were determined using the coefficients of autocorrelation, partial autocorrelation, and cross-correlation in R Studio software. Then, the effective parameters and the optimum combination were determined by the Gamma test method and used to implement the model under three different scenarios in MATLAB software. Gamma test results showed that today's precipitation parameters, precipitation of the previous day, discharge of the previous day, and discharge of two days ago have the greatest effect on the outflow of the basin. Also, the Pt Qt-1 and Pt Pt-1 Qt-1 Qt-2 Qt-3 combinations were selected as the most suitable input combinations for modeling. The results of the modeling showed that in the support vector machine model, the Radial Base kernel Function (RBF) has a better performance than multiple and linear kernels. Also, the performance of the Artificial Neural Network model (ANN) is better than the Support Vector Machine model (SVM) with Radial Base kernel Function (RBF).
F. Golabkesh, A. Nazarpour, N. Ghanavati, T. Babaeinejad,
Volume 26, Issue 2 (9-2022)
Abstract
The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.
B. Shahinejad, A. Parsaei, H. Yonesi, Z. Shamsi, A. Arshia,
Volume 26, Issue 4 (3-2023)
Abstract
In the present study, the flow rate in flues containing lateral semi-cylinders (SMBF) was simulated and estimated under free and submerged conditions using back vector machine models (SVM), spin multivariate adaptive regression (MARS), and multilayer artificial neural network (MLPNN) model. In free flow mode, the dimensionless parameters extracted from the dimensional analysis include the ratio of upstream flow to throat width and contraction ratio (throat width to channel width), and in the submerged state, in addition to these two parameters, the depth-to-throat width, and bottom-depth parameters upstream depth were used as input and the two-dimensional form of flow rate was used as the output of the models. The results showed that in free flow mode in the validation stage, the MARS model with statistical indices of R2 = 0.985, RMSE = 0.008, MAPE = 0.87%, and the SVM model with statistical indices of R2 = 0.971, RMSE = 0.0012, MAPE =1.376%, and MLPNN model with statistical indices of R2 = 0.973, RMSE = 0.011, MAPE = 1.304% have modeled and predicted the flow rate. In the submerged state, the statistical indices of the developed MARS model were R2 = 0.978, RMSE = 0.018, MAPE = 3.6%, and the statistical indices of the SVM model were R2 = 0.988, RMSE = 0.014, 2%. MAPE = 4, and the statistical indicators of the MLPNN model were R2 = 0.966, RMSE = 0.022, and MAPE = 5.7%. In the development of SVM and MLPNN models, radial kernel and hyperbolic tangent functions were used, respectively.
M. Majedi Asl, T. Omidpour Alavian, M. Kouhdaragh, V. Shamsi,
Volume 27, Issue 3 (12-2023)
Abstract
Non-linear weirs meanwhile economic advantages, have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM) and gene expression programming algorithm (GEP) in predicting the discharge coefficient of arched non-linear weirs was investigated using 243 laboratory data series for the first scenario and 247 laboratory data series for the second scenario. The geometric and hydraulic parameters were used in this research including the water load (HT), weir height (P), total water load ratio (HT/p), arc cycle angle (Ɵ), cycle wall angle (α), and discharge coefficient (Cd). The results of artificial intelligence showed that the combination of parameters (Cd, H_T/p, α, Ɵ) respectively in GEP and SVM algorithms in the training phase related to the first scenario (Labyrinth weir with cycle wall angle 6 degrees) were respectively equal to (R2=0.9811), (RMSE=0.02120), (DC=0.9807), and (R2=0.9896), (RMSE=0.0189), (DC=0.9871) in the second scenario (Labyrinth weir with a cycle wall angle of 12 degrees) it was equal to (R2=0.9770), (RMSE=0.0193), (RMSE=0.9768), and (R2 = 0.9908), (RMSE = 0.0128), (DC = 0.9905), which compared to other combinations has led to the most optimal output that shows the very favorable accuracy of both algorithms in predicting the coefficient the Weir discharge is arched non-linear. The results of the sensitivity analysis indicated that the effective parameter in determining the discharge coefficient of the arched non-linear Weir in GEP and in SVM is the total water load ratio parameter (HT/p). Comparing the results of this research with other researchers revealed that the evaluation indices for GEP and SVM algorithms of this research had better estimates than other researchers.
