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The best way for an engineer or scientist to express their knowledge, experience and opinions is day-to-day verbal communication. When a decision needs to be made about an optimal groundwater control system, the decision-making criteria need not always be numerical values. If fuzzy logic is used in multi-criteria decision-making, the criteria are described by linguistic variables that can be represented through fuzzy membership and expert judgment is used to describe such a system. Prior hydrodynamic modeling of the aquifer regime defines the management scenarios for groundwater control and provides an indication of their effectiveness. In this paper, the fuzzy analytic hierarchy process is applied to deal with a trending decision problem such as the selection of the optimal groundwater management system. Linguistic variables are used to evaluate all the criteria and sub-criteria that influence the final decision and the numerical weights of each alternative are determined by mathematical calculations. The paper presents a part of the algorithm - fuzzy optimization in hydrodynamic analysis, which leads to the selection of the optimal groundwater control system. The proposed method is applied in a real case study of an open-cast mine. [ABSTRACT FROM AUTHOR]

This study examines the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM), and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment. The results obtained by the models have been compared. All three models have the relative prediction error below 8%. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1%. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least. [ABSTRACT FROM AUTHOR]