Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.

A methodology based on fuzzy set theory is developed to incorporate imprecise parameters into steady state groundwater flow models. In this case, fuzzy numbers are used to represent parameter imprecision. As such, they are also used as a measure for the uncertainty associated with the hydraulic heads due to the imprecision in the input parameters. The imprecise input parameters may come from indirect measurements, subjective interpretation, and expert judgment of available information. In the methodology, a finite difference method is combined with level set operations to formulate the fuzzy groundwater flow model. This fuzzy modeling technique can handle imprecise parameters in a direct way without generating a large number of realizations. Two numerical solution methods are used to solve the fuzzy groundwater flow model: the groundwater model operator method proposed in this methodology and the iterative algorithm based on conventional interval arithmetics. The iterative method is simple but may overestimate the uncertainty of hydraulic heads. The groundwater model operator method not only provides the hull of the solution set for the hydraulic heads but also considers the dependence of hydraulic head coefficients which are functions of imprecise parameters. Sensitivity analysis shows that the dependence of hydraulic head coefficients has a critical impact on the model results, and neglecting this dependence may result in significant overestimation of the uncertainty of hydraulic heads. A numerical model based on the methodology is tested by comparing it with the analytical solution for a homogeneous radial flow problem. It is also applied to a simplified two‐dimensional heterogeneous flow case to demonstrate the methodology.

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