In this study, the groundwater level of the Kabodarahang aquifer located in Hamadan Province, Iran, is simulated using MODFLOW, Extreme Learning Machine (ELM), and Wavelet-Extreme Learning Machine (WA-ELM) Models. The correlation coefficient and scatter index values for the MODFLOW model are calculated 0. 917 and 0. 0004, respectively. Then, by different input combination and using the stepwise selection, 10 different models are introduced for the ELM and WA-ELM models with different lags. By evaluating all activation functions of the ELM model, the sigmoid activation function predicts groundwater level values with more accuracy. Also, Daubechies2 is selected as the mother wavelet of the WA-ELM models. According to different numerical models results, the WA-ELM model is selected as the superior model in prediction of groundwater level. For the superior model, the correlation coefficient and Nash-Sutcliffe efficiency coefficient are calculated 0. 959 and 0. 915, respectively. These values for ELM model was respectively computed as 0. 828 and 0. 672.

Accurate electricity price forecasting gives a capability to make better decisions in the electricity market environment when this market is complicated due to severe fl uctuations. The main purpose of a prediction model is to forecast future prices. For doing this, the predicted variable (as output) and historical data (as input) should be close to each other. Machine Learning is known as one of the most successful ways of forecasting time series. Extreme Learning Machine (ELM) is a feed-forward neural network with one hidden layer. Hence, in this paper, an Extreme Learning Machine has been used for predicting electricity prices in a medium-term time horizon. The real data of New York City electricity market has been utilized to simulate and predict the electricity price in four seasons of the year. Finally, the fi ndings are compared with multi-layer perceptron (MLP) results, which prove the effi ciency of the model.

Streaming data refers to data that is continuously generated in the form of fast streams with high volumes. This kind of data often runs into evolving environments where a change may affect the data distribution. Because of a wide range of real-world applications of data streams, performance improvement of streaming analytics has become a hot topic for researchers. The proposed method integrates online ensemble Learning into Extreme Machine Learning to improve the data stream classification performance. The proposed incremental method does not need to access the samples of previous blocks. Also, regarding the AdaBoost approach, it can react to concept drift by the component weighting mechanism and component update mechanism. The proposed method can adapt to the changes, and its performance is leveraged to retain high-accurate classifiers. The experiments have been done on benchmark datasets. The proposed method can achieve 0. 90% average specificity, 0. 69% average sensitivity, and 0. 87% average accuracy, indicating its superiority compared to two competing methods.

In this study, the discharge coe cient of rectangular and circular side ori ces was estimated using the Extreme Learning Machine method. Furthermore, in this study for evaluating the ability of di erent ELM models the Monte Carlo simulations are used. The Monte Carlo simulation is a comprehensive classi cation of computational algorithms which uses the random sampling procedure for calculating numerical results. The main idea of this method is based on solving problems which might be actual in nature using random decision-making processes. The Monte Carlo methods are usually used for simulating physical and mathematical systems not solvable with other methods. The Monte Carlo simulation is generally used by probability distribution to solve various problems such as numerical optimization and numerical integration. The k-fold cross validation method is also used for examining the performance of the above models. In this method, the main sample is randomly divided into k sub-samples with the same size. Among k sub-samples, a sub-sample is used as the validation data and the remaining as the test data of the model. Then, the validation process repeats k times (equal to the number of layers) and each of k sub-samples is used exactly once as validation data. In this study, the experimental values obtained by Hussein et al. (2010) and Hussein et al. (2011) are used for validating the results of the numerical models. Their experimental model consisted of a rectangular channel with the length, the width and the height of 9. 15m, 0. 5m and 0. 6m, respectively. They installed the circular and rectangular ori ces at a distance of 5m from the inlet of the main channel on the side wall. In the next stage, the most optimized number of hidden neurons was chosen equal to 30 and the results of all activation functions of the Extreme Learning Machine were examined and the sigmoid activation function is selected for simulating the discharge coe cient. Subsequently, two modeling combinations were introduced using the input parameters as well as  ve di erent Extreme Learning Machine models were developed. The analysis of the modeling results showed that the model with the shape coe cient has more accuracy. The superior model is a function of all input parameters and reasonably estimates values of the discharge coe cient. For example, the values of R and MAPE for this model are estimated 0. 990 and 0. 223, respectively. The results of the superior model were also compared with the empirical equations and it was shown that this model has more accuracy. Also, the partial derivative sensitivity analysis (PDSA) was run for all input parameters.

In this paper, for the first time, the discharge coefficient of triangular plan form weirs is simulated by the Extreme Learning Machine (ELM). ELM is one of the powerful and rapid artificial intelligence methods in modeling complex and non-linear phenomena. Compared to other Learning algorithms such as back propagation, this model acts rapidly in the Learning process and provides a desirable performance in processing generalized functions. In this study, the Monte Carlo simulation is used for examining capabilities of numerical models. Also, the k-fold cross validation method with k=5 is utilized for evaluating abilities of the ELM models. Then, six ELM models are introduced by means of the parameters affecting the discharge coefficient of triangular plan form weirs. After that, the superior model is identified by analyzing the results of the mentioned models. The superior model predicts discharge coefficient values with reasonable accuracy. This model simulates the discharge coefficient as a function of the flow Froude number, vertex angle of the triangular plan form weir, the ratio of weir length to its height, the ratio of flow head to weir height and the ratio of channel width to weir length. For the best model, the Mean Absolute Error, Root Mean Square Error and determination coefficient are computed 1. 173, 0. 012 and 0. 967, respectively. Furthermore, examination of the influence of the input parameters indicates that the flow Froude number is the most influenced factor in modeling the discharge coefficient. Also, the error distribution showed that roughly 86 % of the superior model results had an error less than 2 %. Furthermore, a practical equation was provided to compute the discharge coefficient.

For the first time, in the current study, the discharge coefficient of labyrinth weirs was simulated using the Self-Adaptive Extreme Learning Machine (SAELM) artificial intelligence model in both cases including normal orientation labyrinth weirs (NLWs) and inverted orientation labyrinth weirs (ILWs). The Monte Carlo simulations were also implemented to evaluate the accuracy of the artificial intelligence model. In addition, the validation of the numerical model results was carried out by means of the k-fold cross validation approach. In this study, k was considered equal to 5. First, the most optimized neuron of the hidden layer was computed. The number of the hidden layer neurons was calculated 30. Also, by analyzing the results of different activation functions, it was concluded that the sigmoid activation function has higher accuracy than others. After that, the superior model was identified by conducting a sensitivity analysis. The superior model estimated the discharge coefficient values in terms of all input parameters. This model approximated discharge coefficient values of labyrinth weirs with reasonable accuracy. For example, the values of R2, the Scatter Index and the Nash– Sutcliffe efficiency coefficient for the superior model were calculated 0. 966, 0. 034 and 0. 964, respectively. In addition, the ratio of the total head above the weir to the height of the weir crest (HT/P) and the ratio of length of apex geometry to width of a single cycle (A/w) were identified as the most effective parameters. Finally, a partial derivative sensitivity analysis (PDSA) was conducted for the input parameters.

Objective: In the present era, businesses have developed to a large extent which has, in turn, forced them to manage their resources and expenditures wisely for the sake of competition. This is mainly because the competitive market has severely reduced the flexibility of companies, which means that their ability respond to different economic situations has reduced and this puts most firms at the constant risk of bankruptcy and contraction. Therefore, in this study, we have tried to predict the bankruptcy of manufacturing companies through preventing the occurrence of such risks. Methods: In this study, the "Kernel Extreme Learning Machine" has been used as one of the artificial intelligence models for predicting bankruptcy. Given that Machine Learning methods require an optimization algorithm we have used one of the most up-to-date, "Gray Wolf Algorithm" which has been introduced in 2014. Results: The above model has been implemented on the 136 samples that were collected from the Tehran Stock Exchange between 2015 and 2018. All of the performance evaluation criteria including the classification, accuracy, type error, second-order error and area under the ROC curve showed better performance than the genetic algorithm which was presented and its significance was confirmed by t-test. Conclusion: Considering the gray wolf algorithm’ s high accuracy and its performance compared to the genetic algorithm, it is necessary to use the gray wolf algorithm to predict the bankruptcy of Iranian manufacturing companies either for investment purposes and for validation purposes, or for using internal management of the company.