A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor
Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geologi...
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doaj-06142da7cf80484e95729c7b1049a7192020-11-25T00:24:00ZengMDPI AGWater2073-44412018-11-011011161810.3390/w10111618w10111618A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining FloorDan Ma0Hongyu Duan1Xin Cai2Zhenhua Li3Qiang Li4Qi Zhang5School of Resources & Safety Engineering, Central South University, Changsha 410083, Hunan, ChinaSchool of Resources & Safety Engineering, Central South University, Changsha 410083, Hunan, ChinaSchool of Resources & Safety Engineering, Central South University, Changsha 410083, Hunan, ChinaSchool of Energy Science & Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, ChinaSchool of Resources & Safety Engineering, Central South University, Changsha 410083, Hunan, ChinaState Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, Jiangsu, ChinaWater inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries.https://www.mdpi.com/2073-4441/10/11/1618hazard predictionwater inrushmine floorGA-SVM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dan Ma Hongyu Duan Xin Cai Zhenhua Li Qiang Li Qi Zhang |
spellingShingle |
Dan Ma Hongyu Duan Xin Cai Zhenhua Li Qiang Li Qi Zhang A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor Water hazard prediction water inrush mine floor GA-SVM |
author_facet |
Dan Ma Hongyu Duan Xin Cai Zhenhua Li Qiang Li Qi Zhang |
author_sort |
Dan Ma |
title |
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor |
title_short |
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor |
title_full |
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor |
title_fullStr |
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor |
title_full_unstemmed |
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor |
title_sort |
global optimization-based method for the prediction of water inrush hazard from mining floor |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2018-11-01 |
description |
Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries. |
topic |
hazard prediction water inrush mine floor GA-SVM |
url |
https://www.mdpi.com/2073-4441/10/11/1618 |
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