Application of light gradient boosting machine in mine water inrush source type online discriminant
by Yang Yong; Li Jing; Zhang Jing; Liu Yang; Zhao Li; Guo Ruxue
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 1, 2021

Abstract: Water inrush is a kind of mine geological disaster that threatens mining safety. Type recognition of water inrush sources is an effective auxiliary method to forecast water inrush disaster. Compared with the current hydro-chemistry methodology, it spends a large amount of time on sample collection. Considering this problem, it is urgent to propose a novel method to discriminate water inrush source types online, and further to strive to create more time for evacuation before the disaster. The paper proposes an in-situ mine water sources discrimination model based on light gradient boosting machine (LightGBM). This method combined light gradient boosting (GB) with the decision tree (DT) to improve the network integrated learning ability and enhance model generalisation. The data were collected from in-situ sensors such as pH, conductivity, Ca, Na, Mg and CO₃ components in different water bodies of LiJiaZui Coal Mine in HuaiNan. The results illustrate that the accuracy of proposed method achieves 99.63% to recognise water sources in the mine. Thus, the proposed discriminant model is a timely and an effective way to recognise source types of water in a mine online.

Online publication date: Mon, 15-Mar-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com