Evidence map›Paper›PMID 41436527›Full record

ArticleScientific reports2025

Soil erosion susceptibility assessment in the Bistrița River basin (Romania) using machine learning algorithms and GIS.

George Muratoreanu, Daniel Dunea, Danut Tanislav, Michael Reid, Daniel Constantin Diaconu, Andra Costache, Gheorghe Serban, Ioana Pastor, Petre Bretcan

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Article in Scientific reports, 2025. 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

9 authors.

George MuratoreanuDepartment of Geography, Faculty of Humanities, Valahia University of Targoviste, 130004, Targoviste, Romania.
Daniel DuneaFaculty of Environmental Engineering and Food Science, Valahia University of Targoviste, 13, Aleea Sinaia, 130004, Targoviste, Romania.
Danut TanislavDepartment of Geography, Faculty of Humanities, Valahia University of Targoviste, 130004, Targoviste, Romania.
Michael ReidDepartment of Geography and Planning, School of Humanities, Arts and Social Sciences, University of New England, Armidale, NSW, 2351, Australia.
Daniel Constantin DiaconuFaculty of Geography, University of Bucharest, St. Nicolae Bălcescu, No. 1, 010041, Bucharest, Romania.
Andra CostacheDepartment of Geography, Faculty of Humanities, Valahia University of Targoviste, 130004, Targoviste, Romania.
Gheorghe SerbanFaculty of Geography, Babes-Bolyai University, 5-7 Clinicilor, 400006, Cluj-Napoca, Romania.
Ioana PastorFaculty of Geography, Simion Mehedinti Doctoral School, University of Bucharest, St. Nicolae Bălcescu, No. 1, 010041, Bucharest, Romania.
Petre BretcanDepartment of Geography, Faculty of Humanities, Valahia University of Targoviste, 130004, Targoviste, Romania. petre.bretcan@valahia.ro.

Funding

CNCS/CCCDI-UEFISCDI COFUND-DUT-FEED4FOOD
6 · The paper itself

Abstract

This study evaluates the relationship between soil erosion and 12 environmental and anthropogenic variables to identify erosion-susceptible areas in the Bistrita River basin (Romania), using machine learning algorithms (MLA). Three supervised classification algorithms Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Random Forests (RF) were trained using 4761 sets of values, 1191 values to validate the models, and 1488 to test the models and to ensure that they could be applied in practice. The performance of each model was evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R

Indexed as

Bistrita watershedMachine learning algorithmRUSLESoil erosionSpatial modeling

Identifiers

PMID41436527
PMCPMC12728231

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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.