For water inrush from coal floor, due to different kinds of controlling factors and their internal correlations, the accuracy of prediction model is mostly below expectation. In this paper, it studies on which controlling factors should be selected for water inrush prediction model because all these factors have different influence on water inrush incidents based on the analysis of in situ data. Some factors are proved having limited impacts on water inrush, it is no necessary to collect in situ data of those factors from coal mining work face. Therefore, the workload and expense will decrease. In this paper, an index system of factors influencing water inrush from coal floor is established based on the current water inrush controlling theory and detailed analysis of in situ data obtained from mining regions. Following the Wrapper method in feature selection, 10 main controlling factors were selected from 14 existing indicators which were thought could affect water inrush. After training on dynamic GRU model which is made for water inrush prediction, a comparison among dynamic GRU model and stable SVM and BPMN models turns out the advantages of the previous with a higher accuracy in train, validation and test set against the latter. It is believed GRU model is able to predict water inrush water inrush from coal floor with high accuracy and hence enhances mining safety.