جستجوی مقالات مرتبط با کلیدواژه "انفیس" در نشریات گروه "آب و خاک"

Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers is estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. To do this, firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Next, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Lastly, a computer code was introduced so as to simulate the scour hole around the twin and three piers.

In this study, a hybrid model was developed in order to approximate the discharge coefficient of side weirs located on converging channels for first time. In other words, the ANFIS network was optimized by means of the Firefly Algorithm (FA). After that, six ANFIS and ANFIS-FA models were defined by input parameters. In addition, in this study, the Monte Carlo simulation was employed to study the modeling accuracy. Furthermore, the k-fold cross validation approach was implemented for validating the modeling results. By analyzing the modeling results, it was concluded that hybrid models are more accurate than ANFIS models. The superior model simulated the discharge coefficient values with reasonable accuracy. For example, the values of R2, MAE and RMSE for the superior model were calculated 0.003, 0.011 and 0.015, respectively. Also, about 98% of the superior model results have an error less than 12%. According to the uncertainty analysis results, the superior model had an overestimated performance. A sensitivity analysis indicated that the flow Froude number at the side weir downstream is the most effective input parameter.

In this study, the long-term rainfall in Babolsar city was simulated using an optimized hybrid artificial intelligence (AI) model over a 68 years period during 1951 to 2019. The ANFIS network and the wavelet transform combined to develop the hybrid model. Firstly, the effective lags of time series data were identified by using the autocorrelation function (ACF). Six ANFIS models were defined through these lags, Subsequently. The best ANFIS model was detected by conducting a sensitivity analysis. For testing the best ANFIS model, the value of correlation coefficient (R), variance accounted for (VAF), and scatter index (SI) were respectively computed to be 0.612, 37.029, and 0.761. In Addition, results of the models analysis showed that the (t-1), (t-2), (t-12), and (t-36) were identified as the most significant lags. Finally, the superior hybrid model was examined in three decomposition level (DL), revealing that the best results were obtained from the second decomposition level (DL2). In testing mode of the model, the R, VAF, and SI were calculated to be 0.972, 94.455, and 0.266, respectively. Therefore, the simulation results showed that the wavelet transform enhanced the performance of the ANFIS network significantly.

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.

In this research, an evolutionary based Neuro-fuzzy technique was utilized to estimate the discharge coefficient of labyrinth weirs. In order to optimize the parameters of the adaptive Neuro-fuzzy inference system (ANFIS), the Firefly Algorithm (FFA) was implemented. In modeling the ANFIS-FFA and ANFIS methods, the Monte Carlo simulation was used to evaluate uncertainty of the model. Furthermore, several models with significant flexibility and generalizability were provided using the k-fold cross validation method. First, the input dimensionless parameters including the Froude number (Fr), ratio of the head above the weir to the weir height (HT/p < /em>), cycle sidewall angle (α), ratio of length of the weir crest to the channel width (Lc/W), ratio of length of the apex geometry to the width of a single cycle (A/w) and the ratio of width of a single cycle to weir height (w/p < /em>) were defined. After that, seven different models were introduced for ANFIS and ANFIS-FFA. Then, using a sensitivity analysis, the superior models (ANFIS-FFA 5 and ANFIS 5) and the most effective input parameter (Froude number) were identified. In addition, the error distribution results showed that about 70% of the superior model (ANFIS-FFA 5) results had an error less than 5%. In other words, the superior model had a high statistical significance. Ultimately, the uncertainty analysis for the superior models was carried out.

In this study, the discharge coefficient of sharp-crested weirs located on circular channels was modeled using the ANFIS and ANFIS-Firefly (ANFIS-FA) algorithm. Also, the Monte Carlo simulations (MCs) were used to enhance the compatibilities of the soft computing models. However, the k-fold cross validation method (k=5) was used to validate the numerical models. According to the input parameters, four models of ANFIS and ANFIS-FA were introduced. Analyzing the numerical results showed that the superior model simulated the discharge coefficient as a function of the Froude number (Fr) and the ratio of flow depth over weir crest to the weir crest height) h/P(. The values of the mean absolute relative error (MARE), root mean square error (RMSE) and correlation coefficient (R) for the superior model were calculated 0.001, 0.002 and 0.999, respectively. However, the maximum error value for this study was less than 2%.

The aim of this study was to estimate the ET0 in a moderately cold semi-humid climate in a 22-year statistical period by applying a wavelet-neuro-fuzzy model with a minimum number of input parameters.The results were compared with the ANN and ANFIS models to evaluate the performance of the wavelet-neuro-fuzzy model, The sensitivity analysis of the input parameters was done in three ways: Hill method, coefficient of determination, and StatSoft. Sensitivity analysis showed that temperature (T), Rs, Ra, mean daily wind speed at 2 meters (U2) and Rn were an effective parameter. Based on the results of the sensitivity analysis, six combinations with these parameters were selected.The results indicate that the wavelet-neural-fuzzy model has a better performance than the artificial neural network model. The results also showed that the estimated ET0 value with three inputs parameters of maximum and minimum temperature and solar radiation using fuzzy-neural-wavelet model was more accurate than the neural network. Based on the coefficient of determination and the amount of calculated error for the artificial neural network and the Anfis, use of the combination of 7 input parameters (Ra, Rn, Rs, U2, Tmean, Tmin and Tmax) and four meteorological input parameters (Ra, U2, Tmean and Tmax) lead to more accurate estimates of ET0 in comparison to the FAO Penman-Monteith method. The results also showed that the highest amount of explanatory factor and the lowest error value among the different wavelets used in the fuzzy-neuro-wavelet model were for the 7 and three input parameters (Tmax, Tmin, Rs), respectively.

In this study, the discharge coefficient of the circular side orifices was predicted using a new hybrid method. Combinations made in this study were divided into two sections: 1) the combination of two algorithms including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and providing the PSOGA algorithm 2) using the PSOGA algorithm in order to optimize the Adaptive Neuro Fuzzy Inference Systems (ANFIS) network and providing the ANFIS-PSOGA method. Next, by identifying the parameters affecting on the discharge coefficient of the circular side orifices, 11 different combinations were provided. Then, the sensitivity analysis conducted by ANFIS showed that the Froude number and the ratio of the flow depth to the orifice diameter (Ym/D) were identified as the most effective parameters in modeling the discharge coefficient. Also, the best combination including the Froude number (Fr), the ratio of the main channel width to the side orifice diameter (B/D), the ratio of the orifice crest height to its diameter (W/D) and the ratio of the flow depth to the orifice diameter (Ym/D) for estimating the discharge coefficient was introduced. For this model, the values of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and correlation coefficient (R) were obtained 0.021, 0.020 and 0.871, respectively. Additionally, the performance of the ANFIS-PSOGA method was compared with the ANFIS-PSO and ANFIS methods. The results showed that the ANFIS-PSOGA method for predicting the discharge coefficient was the superior model

Recently, the amount of rainfall underwent serious changes in different areas, particularly in arid and semi-arid regions. Therefore, estimation and pattern recognition of rainfall in a long term period can give sufficient information to hydrologists and water engineers. In this study, for the first time, long-term rainfall pattern in Anzali city for a 67 years period was simulated using a hybrid model so-called “Wavelet-Adaptive Neuro-Fuzzy Inference System” (WANFIS). Rainfalls of 37-, 20- and 10-years period were applied for training, testing and validation of the numerical model, respectively. Firstly, the optimized membership function of the ANFIS network was obtained using the analysis of the numerical results. In other words, the number of optimized membership function was computed to be equal to 8. Then, the various wavelet families were evaluated which the dmey mother wavelet was introduced as the most optimized wavelet family. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WANFIS models were developed. According to the sensitivity analysis, the superior model and effective lags were identified. The superior model estimated the rainfall with high accuracy. For instance, for validation mode of the superior model, the correlation coefficient, scatter index and Nash-Sutcliffe efficiency coefficient were computed to be 0.962, 0.258 and 0.899, respectively.

In this research, the discharge coefficient values of side orifices are modeled using ANFIS and the ANFIS-GA methods. To simulate the discharge coefficient, the effects of the ratio of the main channel width to the side orifice length (B/L), the ratio of the side orifice height to its length (W/L), the ratio of the flow depth in the main channel to the side orifice length (Ym/L) and the Froude number (Fr) were considered. Eleven different models were introduced for each of the ANFIS and ANFIS-GA models to estimate the discharge coefficient. Next, in order to compare the soft computing models’ results with the results of the computational fluid dynamics (CFD), the side orifice discharge coefficient was simulated using FLOW-3D model. To model the flow field turbulence, the standard k-ε and RNG k-ε turbulence models were used. According to the CFD model results, the RNG k-ε turbulence model simulated the flow field turbulence with higher accuracy as compared to k-ε model. Also, the MAPE and RMSE values for the estimated discharges by the CFD model were equal to 12.204 and 0.001, respectively. By analyzing the results of the ANFIS, ANFIS-GA and CFD models, the ANFIS-GA model was introduced as the premier model.

Due to the prolonged droughts in the recent decades, the importance of predicting the flow rate of surface water in rivers for water resources management increases. In this regard, the flow rates in the natural water ways, which is the most important supplement source for water in dam storages, are considered as the most vital factors in predicting surface water. In this study, a combined powerful model, using the artificial neural networks and wavelet theory, was developed to predict the flow rate of the Kor River at the Dashtbal hydrometric station located at the upstream of the Doroudzan Dam. The comparison of the results obtained from this model with those predicted by ANFIS inculcate the superiority of the former providing that the employed parameters are adequately selected. The ANFIS model with 4 Gaussian functions, and the Daubechies 4 wavelet within the third decomposition level occupied the 2nd and 3rd positions in accurate prediction after the combined model developed in this study.

One way to decreases the damage caused by cavitation in spillways is aeration flow using aerators. The required air flow of aerator is one of the most important factors in their design. In this study, to estimate the required air flow of spillway aerators four methods were applied including of stepwise regression, Pfister empirical method, neural network (based on Levenberg- Marquardt algorithm) and the combination of fuzzy-neural (ANFIS). In order to perform of modeling, 914 experimental data on physical model of Clyde Dam spillway and 12 data of Azad Dam related to conducted tests by Water Research Center on Azad dam hydraulic model were gathered. However, the performance and error of these methods were investigated after calculating the required air flow of aerators. The results showed that the combination of fuzzy-neural has the best performance with a root mean square error (RMSE) and correlation coefficient (R) about 0.0194 and 0.968, respectively. In addition, artificial neural network, stepwise regression and Pfister empirical methods had a root mean square error equal to 0.0538, 0.0596 and 1.98, respectively.

Modeling in arid regions to better manage water resources is very important. Groundwater is an important water resource in arid regions. The purpose of this study was to assess the performance of adaptive neuro fuzzy inference (ANFIS) and time series models to predict the water table. In this study, groundwater levels of Shiraz plain for one month ahead were forecasted by using time series models and ANFIS model. In the ANFIS model has been used Gamma and M-test for determine of the optimal input combination and training and testing data length. Performance of different models was compared with the parameters of the error and Taylor diagrams. ANFIS model results showed that this model with membership function of Π-shaped has better performance than the rest of membership functions. Performance comparison of the models indicated very suitable performance of the ARIMA (2, 1, 2) model than ANFIS models with different membership functions.