<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16(1)</volume><submitter>Wei H</submitter><funding>National Natural Science Foundation of China</funding><funding>National Key Research and Development Program of China</funding><journal>Scientific reports</journal><pagination>7450</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12929808</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A coupled spatial reduction-reconstruction and LSTM framework (SRR-LSTM) for groundwater level prediction in large irrigation districts.</pubmed_title><pmcid>PMC12929808</pmcid><funding_grant_id>2024YFC3212300-04</funding_grant_id><funding_grant_id>52479002</funding_grant_id><funding_grant_id>2024YFC3212300-06</funding_grant_id><pubmed_authors>Wang M</pubmed_authors><pubmed_authors>Peng Y</pubmed_authors><pubmed_authors>Li Y</pubmed_authors><pubmed_authors>Yu B</pubmed_authors><pubmed_authors>Xu B</pubmed_authors><pubmed_authors>Wei H</pubmed_authors><pubmed_authors>Wei G</pubmed_authors><pubmed_authors>Liu S</pubmed_authors></additional><is_claimable>false</is_claimable><name>A coupled spatial reduction-reconstruction and LSTM framework (SRR-LSTM) for groundwater level prediction in large irrigation districts.</name><description/><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-09T12:26:25.974Z</modification><creation>2026-07-09T11:17:53.991Z</creation></dates><accession>S-EPMC12929808</accession><cross_references><pubmed>41644726</pubmed><doi>10.1038/s41598-026-37618-4</doi></cross_references></HashMap>