This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, while other portions were used to check and test the generalization ability of the ANFIS model. Water level predictions were made for 24 hours, 48 hours and 72 hours, in which training, checking and testing of the model were performed for each of the prediction periods. The model results from the training, checking and testing data groups show that 48 hours prediction has the least Root Mean Square Error (RMSE) of 0. 05426, 0. 06298 and 0. 05355 for training, checking and testing data groups respectively, showing that the prediction is most accurate for 48 hours.

Nowadays, in turbulent and violate global markets, agility has been considered as a fundamental characteristic of a supply chain needed for survival. To achieve the competitive edge, companies must align with suppliers and customers to streamline operations, as well as agility beyond individual companies. Consequently Agile Supply Chain (ASC) is considered as a dominant competitive advantage. However, so far a little effort has been made for designing, operating and evaluating agile supply chain in recent years. Therefore, in this study a new approach has been developed based on Adaptive Neuro Fuzzy Inference System (ANFIS) for evaluating agility in supply chain considering agility capabilities such as Flexibility, Competency, Cost, Responsiveness and Quickness. This evaluation helps managers to perform gap analysis between existent agility level and the desired one and also provides more informative and reliable information for decision making. Finally the proposed model has been applied to a leading car manufacturing company in Iran to prove the applicability of the model.

In complex engineering problems, there are some inexact conceptions, or a lot of parameters which must be considered. Soft computing is an approach that successfully applied to solve such problems. Determination of fuzzy rules for many problems has not been quite possible by an expert human. In this case, a neuro-fuzzy system which is the combination of neural network (for its ability to learn by datasets) and fuzzy system (for solving the drawback of the neural network) can be enhancing the performance of the system with several parameters or complex conditions. This paper shows the capability of a neuro-fuzzy system namely ANFIS to predicting the shear strength of reinforced concrete beams with steel stirrups. For this propose, the collection of laboratory results which was published in literatures used to train and finally test the proposed system. For this purpose, the sub-clustering approach (SC) applied for generating ANFIS. The results indicated that the considered neuro-fuzzy system was able to predict the shear strength of the RC beams which have been reinforced with steel stirrups.

Accurate determination of the natural gas compressibility factor is crucial for reservoir simulation and material balance computations in petroleum engineering. The data-driven AI techniques, like artificial neural networks, fuzzy systems, and neuro-fuzzy systems, are gaining momentum in estimating fluid properties. An adaptive neuro-fuzzy inference system (ANFIS) is applied here to develop a model to estimate the compressibility factor of two natural gas types. The Takagi-Sugeno fuzzy inference system serves as the foundation for constructing the ANFIS model, where the triangular membership functions are applied. The training data consists of 80% of the available data selected randomly, and the remaining 20% is applied in testing. This developed model is of high accuracy in estimating the compressibility factors of natural gas types, with an average absolute relative deviation of 0.05% and a maximum absolute relative deviation of 0.55% difference between the estimated and experimental value data. Comparing the findings here with the correlations indicates that the ANFIS model in terms of accuracy outperforms its counterparts in this realm.

The implementation of modern ERP solutions has introduced tremendous opportunities as well as challenges into the realm of intensely competent businesses. The ERP implementation phase is a very costly and time-consuming process. The failure of the implementation may result in the entire business to fail or to become incompetent. This fact along with the complexity of data streams has led the researchers to develop a hierarchical multi-level predictive solution to automatically predict the implementation success of ERP solution. This study exploits the strength and precision of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the implementation success of ERP solutions before the initiation of the implementation phase. This capability is obtained by training the ANFIS system with a data set containing a large number of ERP implementation efforts that have led to success, failure, or a mid-range implementation. In the initial section of the paper, a brief review of the recent ERP solutions as well as ANFIS architecture and validation procedure is provided. After that, the major factors of ERP implementation success are deeply studied and extracted from the previous literature. The major influential implementation factors in the businesses are titled as Change Orchestration (CO), Implementation Guide (IG), and Requirements Coverage (RC). The factors represent the major elements that guide the implementation project to a final success or to a possible failure if mismanaged. Accordingly, three initial ANFIS models are designed, trained, and validated for the factors. The models are developed by gathering data from 414 SMEs located in the Islamic Republic of Iran during a threeyear period. Each model is capable of identifying the weaknesses and predicting the successful implementation of major factors. After this step, a final ANFIS model is developed that integrates the outputs of three initial ANFIS models into a final fuzzy inference system, which predicts the overall success of the ERP implementation project before the initiation phase. This model provides the opportunity of embedding the previous precious experiences into a unified system that can reduce the heavy burden of implementation failure.