Evidence map›Paper›PMID 42234180›Full record

ArticleEnvironmental geochemistry and health2026

Predictive Modeling of Mining Laboratory Effluent Contamination Using LSTM-Attention Networks: A Case Study from the Haut-Katanga Copperbelt.

Mathieu Kayembe Musala, Matthieu Tshanga Matthieu, Meschac Amani Mugaruka, Fabrice Kalombo Mwambila, Hervé Ngoie Ilunga, Jerry Mbayo Kyongo, Gbenga Olamide Adesola, Arthur Kaniki Tshamala

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental geochemistry and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Mathieu Kayembe MusalaDepartment of Process Engineering, School of Industrial Engineering, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo.ORCID http://orcid.org/0000-0003-4829-3618
Matthieu Tshanga MatthieuDepartment of Environmental Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Pretoria, South Africa. 69282528@mylife.unisa.ac.za.ORCID http://orcid.org/0009-0005-7223-9867
Meschac Amani MugarukaSchool of Mines, Official University of Bukavu, Bukavu, Democratic Republic of Congo.
Fabrice Kalombo MwambilaEnvironmental Analysis Laboratory, Polytechnic Faculty, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo.ORCID http://orcid.org/0009-0001-8840-8910
Hervé Ngoie IlungaDepartment of Process Engineering, School of Industrial Engineering, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo.
Jerry Mbayo KyongoDepartment of Process Engineering, School of Industrial Engineering, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo.
Gbenga Olamide AdesolaDepartment of GIS and Remote Sensing, University of Fort Hare, King Williams, South Africa.
Arthur Kaniki TshamalaEnvironmental Analysis Laboratory, Polytechnic Faculty, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo.ORCID http://orcid.org/0000-0003-1411-8408

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mining laboratory effluents in the Democratic Republic of Congo (DRC) represent an undercharacterized source of heavy metal contamination, with direct implications for receiving watershed quality and downstream community health. A comparative time-series modeling framework is applied, incorporating ARIMA as a statistical baseline, alongside Vanilla LSTM and a dual-layer LSTM-Attention architecture, to predict effluent concentrations of pH, suspended solids (SS), and six heavy metals (As, Cu, Fe, Zn, Pb, Ni) from a mineral analysis laboratory in Haut-Katanga. The dataset comprises 43 weekly observations (January to October 2023), with 80% used for training (n = 34) and 20% for testing (n = 9). The LSTM-Attention model demonstrates the highest predictive performance (R

Indexed as

Environmental MonitoringMetals, HeavyMiningWater Pollutants, ChemicalDemocratic Republic of the CongoHydrogen-Ion ConcentrationLaboratoriesLong Short Term MemoryPrediction AlgorithmsPredictive Learning ModelsMetals, HeavyWater Pollutants, ChemicalDRC Mining CodeEnvironmental pollutionHeavy metals contaminationLiquid effluentMineral analysis laboratoryTime series forecasting

Identifiers

PMID42234180
PMCPMC13233990

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.