UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine
IR@CIMFR: CSIR-Central Institute of Mining and Fuel Research, Dhanbad
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| Title |
UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine
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| Creator |
Kumari, K.
Dey, Prasanjit Kumar, Chandan Pandit, Dewangshu Mishra, S.S. Kisku, Vikash Chaulya, S.K. Ray, S.K. Prasad, G.M |
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| Subject |
Instrumentation
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| Description |
A uniform manifold approximation and projection (UMAP) and long short-term memory (LSTM) deep learning model have been proposed to forecast a sealed-off area's fire status in underground coal mines. It protects miners' life by providing early warning to the miners regarding the impending mine hazards. The proposed forecasting model graphically displays fire status in the form of Ellicott's extension graph. An experiment has been conducted to measure the proposed forecasting model's efficiency and two existing machine learning models, namely support vector regression (SVR) and auto-regressive integrated moving average (ARIMA) models. It has been found that gas concentration prediction of the proposed UMAP-LSTM model has the lowest root mean square error of 0.288, 0.006, 0.0995, 0.902, 0.238, 0.452, and 0.006 for O2, CO, CH4, CO2, H2, N2, and C2H4 gases respectively than the existing SVR and ARIMA models, which indicates higher efficiency of the proposed prediction model.
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| Publisher |
Elsevier
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| Date |
2021-02
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| Type |
Article
PeerReviewed |
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| Identifier |
Kumari, K. and Dey, Prasanjit and Kumar, Chandan and Pandit, Dewangshu and Mishra, S.S. and Kisku, Vikash and Chaulya, S.K. and Ray, S.K. and Prasad, G.M (2021) UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine. Process Safety and Environmental Protection, 146. pp. 837-852.
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| Relation |
http://cimfr.csircentral.net/2298/
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