Kybernetes

Purpose – The purpose of this paper is to attempt to establish the pattern of multi-objective and multi-dimensional grey fuzzy forecasting with feedback based on the theories of grey system and fuzzy recognition.

Design/methodology/approach – First, according to the given weights, the weighting integrated value of samples were computed. Second, the method of fuzzy recognition with single index was employed to calculate the fuzzy classification of the integrated value. According to the cause analysis, the fuzzy classification of the integrated value is used to compute the weights of indexes. In the same way, repeating the above processes, the weighting integrated value and fuzzy classification with given accuracy are retrieved at the same time. Finally, the authors calculate the correlation coefficient between the weighting integrated values and forecasting objects, according to the principle of maximal relativity, optimizing the weighting integrated value of samples, establishing the fuzzy forecasting pattern, and checking the model's precision. A numeric example is also computed in the last part of the paper.

Findings – The results are convincing: not only that the pattern of multi-objective and multi-dimensional grey fuzzy forecasting with feedback based is valid, but also the model's applied prediction accuracy is higher, where the test samples' mean forecast accuracy of groundwater dynamic levels is 96.50 percent.

Practical implications – The method exposed in the paper can be used to predict groundwater dynamic levels and even for other similar forecast problems.

Originality/value – The paper succeeds in realising both a prediction pattern and application of predicting groundwater dynamic levels by using the newest developed theories of grey system and fuzzy recognition.