Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.
- DOI
- 10.1016/j.scitotenv.2019.134230
- Published
- 2020 Jan 10
- Container
- The Science of the total environment
- Publisher
- Not recorded
- Open access
- no
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1016/j.scitotenv.2019.134230,
title = {Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.},
author = {Rahmati O and Falah F and Dayal KS and Deo RC and Mohammadi F and Biggs T and Moghaddam DD and Naghibi SA and Bui DT},
year = {2020},
journal = {The Science of the total environment},
doi = {10.1016/j.scitotenv.2019.134230},
url = {https://doi.org/10.1016/j.scitotenv.2019.134230}
}RIS
TY - JOUR TI - Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. AU - Rahmati O AU - Falah F AU - Dayal KS AU - Deo RC AU - Mohammadi F AU - Biggs T AU - Moghaddam DD AU - Naghibi SA AU - Bui DT PY - 2020 JO - The Science of the total environment DO - 10.1016/j.scitotenv.2019.134230 UR - https://doi.org/10.1016/j.scitotenv.2019.134230 ER -
APA
O, R., F, F., KS, D., RC, D., F, M., T, B., DD, M., SA, N., & DT, B. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia.. The Science of the total environment. https://doi.org/10.1016/j.scitotenv.2019.134230
Source records
- pubmed · retrieved 2026-09-25T15:12:02.634Z
- europe-pmc · retrieved 2026-09-25T15:12:02.650Z