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

## TL;DR

This paper proposes a GRNN algorithm to predict missing ERT data in coal mines caused by electrode disconnection, improving data accuracy for safety monitoring.

## Contribution

A GRNN-based method is introduced for predicting missing ERT data in coal mines, achieving higher accuracy than traditional interpolation methods.

## Key findings

- GRNN predicted data accuracy reaches 91.46% when original data integrity is 82.96%.
- The method achieves 82.45% accuracy even when data integrity is as low as 55.56%.
- In real coal mining applications, GRNN outperforms mean value interpolation by 14.99% in accuracy.

## 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 data are predicted using the GRNN algorithm. The results of verification experiments conducted in a water tank show that when the original data integrity is 82.96%, the predicted data accuracy reaches 91.46%, and when the original data integrity is only 55.56%, the predicted data accuracy still reaches 82.45%. Finally, actual applications of the proposed method are carried out on coal mining faces. A set of data with an integrity of 73.8% is predicted. Compared with the measured data when all the electrodes are online, the accuracy of the predicted data is 85.18%. The accuracy of the data predicted using the proposed method is 14.99% higher than that of the data predicted using the commonly used mean value interpolation method.