Research on missing value prediction of measured ERT data for coal mine based on a GRNN algorithm.

ABSTRACT: In the process of long-term monitoring of the coal seam floor of a coal mining face using electrical resistivity tomography (ERT), the data loss caused by electrode disconnection adversely affects early warning of water inrush and prevents the identification of hidden dangers, hindering safe production. Due to the particularity of the monitored environment, the maintenance of offline electrodes may not be timely. Therefore, how to deal with the loss of measured data caused by electrode disconnection has become a problem that must be solved in the long-term monitoring process. In this paper, we analyze the effect of electrode disconnection on the measured data. Then, the principle of the general regression neural network (GRNN) algorithm is introduced. The missing values in the measured dat

SUBMITTER: Wang P 

PROVIDER: S-EPMC12799012 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature