An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines.

Boyu Luan, Wei Zhou, Izhar Mithal Jiskani, Zhiming Wang

Abstract read

In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Research on blasting mechanism and blasting effect of aqueous media in open pit coal mines.Scientific reports · 2023
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Boyu LuanState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China.ORCID 0000-0003-3924-2210

Wei ZhouState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China.

Izhar Mithal JiskaniDepartment of Mining Engineering, National University of Sciences & Technology, Balochistan Campus, Quetta 87300, Pakistan.ORCID 0000-0002-3220-8880

Zhiming WangState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 μg/m

Indexed as

Air PollutantsDustCoalEnvironmental MonitoringMachine LearningParticulate MatterAir PollutantsCoalDustParticulate Matterdust pollutionestimationMarkov chainsopen-pit coal minerandom forest

Identifiers

PMID36674111

DOI10.3390/ijerph20021353

PMCPMC9859337

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.