Dynamic input domain reduction for test data generation with iterative partitioning

Nikravan, Esmaeel; Parsa, Saeed

doi:10.1504/ijcse.2020.105213

Cited by 1 publication

(1 citation statement)

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“…In fact, these above methods have their own limitations. After all, there are two main problems which need to be further explored (Zhang and Chen, 2020;Nikravan and Parsa, 2020;Wang et al, 2019;Thwe and Thaw, 2019;Zhao et al, 2019):…”

Section: Introductionmentioning

confidence: 99%

Application of light gradient boosting machine in mine water inrush source type online discriminant

Yang

Zhang

et al. 2021

IJCSE

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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 10 Y. Yong et al. 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 CO 3 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.

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