anfis

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 Iran, the climatic conditions are such that even in the rainiest areas of the country, there is a need for groundwater resources, and this demand is increasing every year. Since, groundwater is one of the most valuable water resources in Iran, it is very necessary to predict its changes in order to use it optimally with the aim of sustainable development. One of the most complex hydrological processes in nature is the rainfall-groundwater level process, which is affected by various physical and hydrological parameters. Although, various models have been presented to predict the changes in the groundwater level using the rainfall patterns, but less attention has been paid to the transfer function model. Hence, the main objective of this research is to introduce and use the transfer function (TF) model to predict the monthly groundwater level using rainfall data and to compare its results with ANFIS and Artificial Neural Network (ANN) models.In the present study, 30-year data (1992-2021) of meteorological stations and observation wells in 3 watersheds of Galikesh, Ramian and Mohamadabad were used to model the rainfall and groundwater level. of the Gorganroud river basin.Then, considering that the years closer to the present time have more accurate information about the situation of this time, the years were considered as a forward process in artificial neural networks. The model fitting and prediction of the groundwater level values using rainfall data for the next 12 months was performed with applying three models: Artificial Neural Network (ANN), Adaptive neuro fuzzy inference system (ANFIS), and transfer function (TF). For this purpose, MINITAB SAS, SPSS, and R software were used. Next step, the validation of the values predicted by the models was evaluated using three indices Mean Absolute Distance (MAD), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).The results of the autocorrelation plots of the groundwater level of the wells revealed that all time series have a seasonal trend with a period of 12 months. Based on the cross-correlation plots, it was also found that rainfall has direct effect on the groundwater level in the two watersheds of Galikesh and Mohamadabad with lag time of three months and in the Ramian watershed with a delay of one month. The validation results of the models using three indexes MAD, RMSE and MAPE revealed that the artificial neural network model for predicting the groundwater level using monthly rainfall data in all three investigated watersheds had the most appropriate performance (RMSE=0.0778, 0.0243, 0.0532m) and the ANFIS model is ranked second (RMSE=0.1841, 0.0832, 0.1012m). Although the transfer function model was less accurate than the other two methods (RMSE=0.5711, 0.5023, 0.3234m), but this model has performed well in fitting the monthly groundwater level values. This model is very effective in identifying the delay in the impact between the input and output variables, as well as expressing the model based on which the impact of rainfall can be expressed as a model.The results of this research show that all three models of artificial neural network, ANFIS and transfer function can be used to predict the groundwater level using monthly rainfall values. Consecutive overestimation and underestimation, which increases the error and decreases the performance of the models, was not observed for the three used models. Also, all three models perform well in detecting trends and data changes. However, the artificial neural network model is more accurate than the other models. In addition, when forward process is used in artificial neural network modeling, compared to the case where the complete series of data is used, the efficiency of the model is significantly improved.

The demand for freshwater is increasing, while the limited water resources are subject to over-harvesting, pollution, and climate change, which require improving water resource management to distribute it equitably and achieve It highlights the goals of sustainable development. A low-cost option to support better water management strategies is to develop models capable of predicting available water amounts, especially amounts related to precipitation and river flow. Climatic diversity and climate changes are basic assumptions for hydro climatological predictions. One of the remarkable aspects of this issue is the correlation between large-scale atmospheric-oceanic phenomena or Teleconnection patterns with hydrological processes on a local scale, and these patterns can also affect the inflow to the dams. This study uses three machine learning models, an artificial neural network, a Bayesian neural network, and an adaptive neuro-fuzzy inference system to predict dam inflow and evaluate their efficiency. For this purpose, 12 scenarios consisting of rainfall variables, inflow to the dam, and nine climatic indicators with a delay of up to six-time steps were designed to investigate the effect of using long-term models as predictive variables of the flow one month later in Amirkabir Dam. to be placed The analysis of the results of this research showed that the use of the Nino3.4 index with one-time step delay as well as the PDO index with two-time step delays can increase the accuracy of the model compared to the scenarios in which only station variables are used. to be According to the results, the Nino 3.4 index was found to be the most effective index on the inflow to Amirkabir Dam, and the scenario in which the mentioned index along with the rainfall and flow data of one and two months before was used as input, in all three The model recorded the highest accuracy. Also, the performance of the ANFIS model for the mentioned scenario (scenario 9), with RMSE and R2 values, equal to 5.69 and 0.79 cubic meters per second, respectively, was better than the ANN and BNN models, so the value of the R2 index for the best scenario consisting of station variables (scenario 5), it increased by 0.15 and the value of RMSE index decreased by 0.78 cubic meters.

In free overfall spillways, waterfalls over the crown of the spillway almost vertically and impacts the downstream bed of the dams. Due to the high velocity and energy of the flow which impacts the erodible downstream bed, it may cause scouring close to the foundation of the dam and consequently threaten the stability of the dam.

In this study, artificial intelligence methods were used to estimate the scour depth of slope control structures with sharp-crested weir due to the complexity of the phenomenon. Three models including neural network, adaptive fuzzy neural system, and support vector machine (SVM) were used as artificial intelligence or black-box model to solve the problem..

The results showed that artificial intelligence methods are more efficient than conventional experimental methods in estimating the depth of downstream scours of slope control structures with sharp-crested weir. Using more parameters in the input of artificial intelligence models does not increase the accuracy of these models. It is because of increasing errors as a result of using more parameters in these models. In estimating the downstream scour depth of slope control structures with the sharp-crested weir in both calibration and validation stages, an adaptive fuzzy neural system model is up to 20% more reliable than the artificial neural network model and up to 8.5% than the support vector machine model.

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.

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

Groundwater is the most valuable water resources in any region and in many arid and semi-arid regions of the world, such as Iran, is the main source for drinking and agricultural needs. In recent years, with the increase in population and as a result of increasing withdrawals from aquifers and climate change, many of aquifers are in poor condition, and these conditions continue or are deteriorating. In this regard, regular monitoring of aquifers is always very important and by making appropriate management decisions, it is possible to prevent more damage to aquifers and reduce the damage. The purpose of this study is to determine the droughts of the future and to determine its impact on the aquifer of Shahrekord plain. In this study, using the output of CMIP6 models, climatic variables such as rainfall and temperature for the next period are simulated and the rainfall situation in the region until 2100 has been determined. Then, using the ANFIS model, groundwater depth in five selected piezometers in the plain is predicted by 2050. According to the results of this study, the aquifer condiotion of Shahrekord plain has been determined by 2050 and it has been determined that in some parts of Shahrekord plain, the groundwater depth will increase to 26 meters. Due to possible changes in the future in order to prevent the situation from deteriorating and increasing the damage, appropriate management decisions must be made in this regard.

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.

Water scarcity and subsequent social and economic crises have doubled the need to use new interdisciplinary knowledge-based approaches in reforming water resources management structures and policies and implementing water resources plans and projects. In the meantime, recognizing the interrelationships of the social sciences and their water resources systems has become a problem that its solution help us to correct existing dysfunctional structures. Therefore, the main purpose of this study is to prove the interrelationship of some social parameters and water resources on a continental scale with the use of software modeling tools. In this study, per capita data on renewable water resources and social parameters including the ratio of rural population to urban population, population density, number of Internet users and education index on an annual scale are considered. The statistical period of the data was 13 years (2005-2007) and this study was performed for 42 countries in Asia whose per capita water resources were declining. Then, using soft copmuting methods such as Artificial Neural Network (ANN), Decision Tree (M5) and Adaptive Fuzzy-neural Inference System (ANFIS), the interrelationship between per capita water resources and social parameters was modeled. Modeling results were evaluated by the criteria of determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) values. Finally, the results indicate the superior performance of ANFIS method compared to the other two models in evaluating the interaction of per capita water resources and social parameters. Also, after the ANFIS model, the M5 and ANN models had better performance, respectively.

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.

The increase in dust storms occurrence in recent years in southwestern Iran, especially in Khuzestan province, and consequently the decrease in air quality in these areas, has doubled the importance of forecasting and linking this phenomenon with climate variations. The aim of this study was to investigate the efficiency of hybrid Genetic-Annealing (GA-SA) and Genetic Algorithm (GA) methods for selecting optimal input variables in forecasting the frequency of days with dust storm (FDSD). For this purpose, hourly dust data and meteorological organization codes, as well as climatic data including maximum temperature, minimum temperature, average temperature, total rainfall and maximum wind speed on a seasonal scale with a statistical period of 35 years (1984-2018) in seven synoptic stations in Khuzestan province were used. Then, by having a time series of FDSD index and other climatic variables, it was compared to the efficiency of different modes of input variables, in order to forecasting the frequency of days with dust storm in the next season. The results showed that the hybrid Genetic-Annealing method (GA-SA) had the best performance among all the modes of selecting the input variables; In this case, the evaluation criteria of R, MAE and RMSE varied from 0.91, 0.29, and 0.44 to 0.99, 0.13 and 0.17 in the studied stations, respectively. Also, the average frequency of days with dust storm on a seasonal scale in the studied stations varied from 1.68 to 4.10, respectively, so that with increasing FDSD index in the study station, the predictive accuracy of all modes increased so that in the first forecast state (based solely on the FDSD index), the correlation coefficient between the observational values of the days associated with dust storms and its computational values increased from 0.87 to 0.95. For the second case (forecast based on frequency of days with dust storm and all Auxiliary Characteristics, ie FDSD & AC), the third mode (based on the optimization of the Genetic Algorithm) and the fourth mode (based on the hybrid Genetic-Annealing method) the correlation coefficient also varied from 0.93 to 0.94, 0.91 to 0.97 and 0.94 to 0.99 in the studied stations, respectively. In general, by comparing the modes used, the hybrid Genetic-Annealing method (GA-SA) performed the best, followed by the Genetic Algorithm (GA). The results of this study can be useful in managing the consequences of dust storms and desertification programs in the study areas.

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%.

Nowadays, monitoring of river quality information is one of the most important issues in water resources engineering because of the direct relationship of water quality with environmental health and quality of life. Today, traditional methods of river monitoring are receiving less attention due to the fact that they are costly and time-consuming for the researcher. Instead, the recent, low-cost methods are favorable to many researchers in this filed. Different methods have always been considered for river monitoring, but the application of spectral indicators and remote sensing technologies to control and monitor the water quality of rivers and reservoirs is very cost-effective and could be a good alternative to traditional methods. Since it is time-saving and less costly, it would be a good indicator for the whole region and a good alternative to manual methods (Bonansea et al., 2015). Although satellite imagery has been widely used in estimating water quality indices (Onderka and Pekárová, 2008), the complexity of hydrological systems and the presence of noise in images can increase the calculation error. Wavelet transform and intelligent models are among the most efficient methods that can significantly increase computation accuracy by filtering and noise reduction. Good research has been done on the use of wavelet transform in image processing (Graps, 1995) and fuzzy inference system to estimate water quality parameters (Solgi et al., 2017). In this study, using wavelet transform, Landsat 8 images were processed, then the processed images were considered as inputs of ANFIS model.

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 general, rapid transformation of supercritical flow regime into subcritical flow is accompanied with hydraulic jump. The phenomenon usually occurs at downstream of the hydraulic structures such as ogee spillway. The length of hydraulic jump is one of the most important parameters used to determine the dimension of stilling basins. In current study, a hybrid method for predicting the hydraulic jump length on sloping rough bed was developed. In the other words, the hybrid method (ANFIS-GA) was presented using combination of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithm (GA). In this study, to examine the performance of ANFIS-GA models, the Monte Carlo simulation (MCs) was used. At first, the effective parameters on length of hydraulic jump such as; Froude number in upstream of the hydraulic jump, the ratio of bed roughness, sequent depth ratio, and bed slope, were identified. Next, regarding these parameters, five ANFIS-GA models were defined. Then, the results of the ANFIS-GA models were examined and the superior model was introduced. The superior model predicted the experimental measurements with acceptable accuracy. For example, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were respectively computed 4.520 and 0.781. In addition, the results of modeling revealed that the Froude number at upstream of hydraulic jump was the most effective parameter in modeling the length of hydraulic jump on sloping rough bed using ANFIS-GA method.

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.