<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The global ecological environment is fragile, and large-scale coal mining has accelerated the loss of water resources, but it is difficult to quantify its impact. Studies have found that large-scale coal mining has a particularly large impact on the underflow zone of rivers, but blind mining affects environmental damage and loses the water volume of the river. The lack of a compensation mechanism for water damage caused by coal mining often exacerbates the province’s fragile water environment. This paper analyzes the prediction of the impact of coal mining on the underflow zone of the river. First of all, determine the mining area of the mine and collect and collect data. Then, the lost water volume was calculated as a predicted score using the double integro-differential equation. Finally, the GA-BiLSTM model is proposed to predict the lost water capacity. The experimental results show that the mean absolute error (MAE), root mean square (RMSE), and prediction pass rate are 11.78/%, 24.87/%, and 92/%, respectively, and the average relative error is only 9.98%. Compared with BP, SVM, LSTM, Bi-LSTM neural network model has better prediction accuracy. The experimental results also show that the model has good reliability and practicability, which can provide a new idea and method for the new analysis of the impact of coal mining on the underflow zone of the river.</p>
</abstract>

The global ecological environment is fragile, and large-scale coal mining has accelerated the loss of water resources, but it is difficult to quantify its impact. Studies have found that large-scale coal mining has a particularly large impact on the underflow zone of rivers, but blind mining affects environmental damage and loses the water volume of the river. The lack of a compensation mechanism for water damage caused by coal mining often exacerbates the province’s fragile water environment. This paper analyzes the prediction of the impact of coal mining on the underflow zone of the river. First of all, determine the mining area of the mine and collect and collect data. Then, the lost water volume was calculated as a predicted score using the double integro-differential equation. Finally, the GA-BiLSTM model is proposed to predict the lost water capacity. The experimental results show that the mean absolute error (MAE), root mean square (RMSE), and prediction pass rate are 11.78/%, 24.87/%, and 92/%, respectively, and the average relative error is only 9.98%. Compared with BP, SVM, LSTM, Bi-LSTM neural network model has better prediction accuracy. The experimental results also show that the model has good reliability and practicability, which can provide a new idea and method for the new analysis of the impact of coal mining on the underflow zone of the river.

Quantitative Analysis of the Influence of Coal Mining on the Underflow Zone of Rivers by Using Double Integro-differential Equations

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The global ecological environment is fragile, and large-scale coal mining has accelerated the loss of water resources, but it is difficult to quantify its...

Quantitative Analysis of the Influence of Coal Mining on the Underflow Zone of Rivers by Using Double Integro-differential Equations

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 1325 - 1338

Eingereicht: 01. Feb. 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00038

Schlüsselwörter
Coal mining, River underflow zone, Double integro-differential equation, GA-BiLSTM model

© 2023 Yuan Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Quantitative Analysis of the Influence of Coal Mining on the Underflow Zone of Rivers by Using Double Integro-differential Equations

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 1325 - 1338

Eingereicht: 01. Feb. 2022

Akzeptiert: 05. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00038

SchlüsselwörterCoal mining, River underflow zone, Double integro-differential equation, GA-BiLSTM model

© 2023 Yuan Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Coal mining, River underflow zone, Double integro-differential equation, GA-BiLSTM model