A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network. (20th June 2022)

• Record Type: Journal Article
• Authors: Zhu, Zhijie; Sun, Chen; Gao, Xicai; Liang, Zhuang
• Other Names: Wen Zhijie Academic Editor.
• Is Part Of: Geofluids. Volume 2022(2022)
• Publication Date: 2022-06-20
• Subjects: Hydrogeology -- Periodicals; Sedimentary basins -- Periodicals; Fluids -- Migration -- Periodicals; Groundwater flow -- Periodicals; Geothermal resources -- Periodicals; Fluid dynamics -- Periodicals; Earth -- Crust -- Periodicals; 551.49
• DOI: 10.1155/2022/9430526
• Languages: English
• ISSNs: 1468-8115

Abstract

Abstract : The research was aimed at predicting floor water-inrush risk in coal mines and forewarn of such accidents to guide safe production of coal mines in practice. To this end, a prediction method for floor water inrush combining the chaotic fruit fly optimization algorithm (CFOA) and the generalized regression neural network (GRNN) is proposed. Floor water inrush is predicted by virtue of the robust nonlinear mapping capability of the GRNN. However, because the prediction effect of the GRNN is influenced by the smoothing factor, the CFOA is adopted to optimize this factor. In this way, influences of human factors during parameter determination of the GRNN prediction model are decreased, and the prediction accuracy and applicability of the model are improved. Results show that the CFOA–GRNN prediction model has an accuracy of 93.2% for whether floor water inrush will occur or not. Compared with the BPNN, RNN, and GRU network prediction model, the CFOA–GRNN model is superior in the prediction accuracy and generalization, and it can more accurately predict floor water inrush.