Developing a new model based on neuro-fuzzy system for predicting roof fall in coal mines

Roof fall is one of the most important problems connected with underground coal mines because it plays a significant role in financial and human losses. Hence, it is essential to accurately predict the roof fall rate for the purpose of controlling, reducing, and/or even eliminating the risk of the involved problems. On the other hand, there are many different parameters that make a considerable impact on the occurring roof rate. Most of these factors are not completely known or measurable. Therefore, the problem of predicting roof fall is vague, sophisticated, and complex. Adaptive neuro-fuzzy inference system (ANFIS) is a powerful and robust tool for modeling linear and non-linear patterns in science and engineering problems. In this paper, the ANFIS system is applied to model the roof fall rate in coal mines. The constructed model uses the subtractive clustering method to generate fuzzy rules based on 109 data of roof performance from US coal mines. The results demonstrate that prediction of roof fall rate by the ANFIS model is satisfactory.