A clustering-based surrogate model leveraging random forest for rapid pumping optimization in saltwater-intruded heterogeneous aquifers

A clustering-based surrogate model leveraging random forest for rapid pumping optimization in saltwater-intruded heterogeneous aquifers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A clustering-based surrogate model leveraging random forest for rapid pumping optimization in saltwater-intruded heterogeneous aquifers Ali Ranjbarzeinalygharamaleki, Claudia Cherubini, Tom Baldock This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8715910/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The efficient approximation of time-variant outputs from high-fidelity numerical models is essential for sustainable groundwater management in coastal aquifers. While surrogate models are increasingly deployed to bypass the computational costs of simulation-optimization (S-O) under heterogeneity, their performance often degrades in high-dimensional input spaces. This study proposes a novel ensemble clustering framework integrating hierarchical algorithms coupled with a Random Forest (RF) surrogate model to optimize pumping strategies across extensive well networks. The framework utilizes MODFLOW and SEAWAT to generate a foundational dataset of hydraulic drawdown and saltwater intrusion (SI) distributions. A primary innovation lies in our clustering-based dimensionality reduction, which effectively reduces 52 physical pumping wells to 10 representative proxy wells. This strategy significantly reduces input dimensionality while identifying near-optimal pumping patterns. To train the RF model, targeted SEAWAT simulations were subsequently implemented to generate 5,000 training samples, for each of which 100 realizations of hydraulic conductivity fields were generated. Results indicate that this integrated clustering-RF approach achieves 95% computational savings over traditional surrogate-numerical hybrids. This efficiency is realized through a drastic reduction in input variables via well-field classification and focused sampling near optimal extraction patterns. The resulting scalable framework provides a robust tool for decision-makers managing complex, saltwater-intruded aquifer systems. Saltwater intrusion Surrogate model Ensemble Clustering Random Forest Full Text Supplementary Files 1.png 11.png 12.png 13.png 14.png 2.png 3.png 3Dparetonew2.xlsx 3dparetonew.xlsx 4.png 5.png Competingintereststatement.docx Graphicalabstract.docx M5final.xlsx MT3D.cnf MT3D001.mas MT3D001.ucn MT3D002.mas MT3D002.ucn Qom.CellSummary.csv Qom.nm Qom.os Qom.r Qom.w Qom.ws Qom.ww Qom.adv Qom.asp Qom.asp1 Qom.ba6 Qom.btn Qom.ccf Qom.chd Qom.chob Qom.dis Qom.drw Qom.dsp Qom.dss Qom.evt Qom.gbob Qom.gcg Qom.ghb Qom.glo Qom.gpr Qom.h5 Qom.hed Qom.hed.h5 Qom.hff Qom.hob Qom.in1 Qom.ins Qom.lmt Qom.lpf Qom.m2p Qom.mfn Qom.mfn1 Qom.mfr Qom.mfs Qom.mfx Qom.mtr Qom.mts Qom.obs Qom.oc Qom.ocn Qom.out Qom.param Qom.pcg Qom.pes Qom.pst Qom.rch Qom.riv Qom.rmf Qom.sip Qom.snn Qom.snn1 Qom.ssm Qom.tsnn Qom.tsnn.kp Qom.tob Qom.tpl1 Qom.wel Qom001.con Qom002.con Qombat1.bat Socialchoice2.xlsx Socialchoice.xlsx Verifiedsecanrio.xlsx authoragreemtnstatement.docx m5data.xlsx mt3dms53.exe nashrobysut.xlsx p. pest.dap photo20260128131807.jpg photo20260128131816.jpg photo20260128131823.jpg photo20260128131827.jpg photo20260128131832.jpg photo20260128131848.jpg photo20260128131851.jpg photo20260128131854.jpg photo20260128131858.jpg photo20260128131902.jpg photo20260128131905.jpg photo20260128131908.jpg photo20260128131910.jpg photo20260128131913.jpg photo20260128131916.jpg photo20260128131922.jpg photo20260128131925.jpg photo20260128131928.jpg photo20260128131931.jpg photo20260128131934.jpg photo20260128132006.jpg photo20260128132010.jpg photo20260128132013.jpg photo20260128132016.jpg photo20260128132020.jpg photo20260128132023.jpg photo20260128132026.jpg photo20260128132029.jpg photo20260128132031.jpg pslave.rdy qom.cnd qom.drf qom.jst qom.mtt qom.par qom.rei qom.res qom.rmr qom.rst qom.sen qom.seo M5final.xlsx m5data.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 01 Apr, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 17 Feb, 2026 Editor assigned by journal 09 Feb, 2026 First submitted to journal 05 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8715910","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592868853,"identity":"002786a4-bcec-4573-bfeb-5d7c0df0b94d","order_by":0,"name":"Ali 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While surrogate models are increasingly deployed to bypass the computational costs of simulation-optimization (S-O) under heterogeneity, their performance often degrades in high-dimensional input spaces. This study proposes a novel ensemble clustering framework integrating hierarchical algorithms coupled with a Random Forest (RF) surrogate model to optimize pumping strategies across extensive well networks. The framework utilizes MODFLOW and SEAWAT to generate a foundational dataset of hydraulic drawdown and saltwater intrusion (SI) distributions. A primary innovation lies in our clustering-based dimensionality reduction, which effectively reduces 52 physical pumping wells to 10 representative proxy wells. This strategy significantly reduces input dimensionality while identifying near-optimal pumping patterns. To train the RF model, targeted SEAWAT simulations were subsequently implemented to generate 5,000 training samples, for each of which 100 realizations of hydraulic conductivity fields were generated. Results indicate that this integrated clustering-RF approach achieves 95% computational savings over traditional surrogate-numerical hybrids. This efficiency is realized through a drastic reduction in input variables via well-field classification and focused sampling near optimal extraction patterns. The resulting scalable framework provides a robust tool for decision-makers managing complex, saltwater-intruded aquifer systems.\u003c/p\u003e","manuscriptTitle":"A clustering-based surrogate model leveraging random forest for rapid pumping optimization in saltwater-intruded heterogeneous aquifers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 22:25:13","doi":"10.21203/rs.3.rs-8715910/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2026-04-02T02:28:29+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-02-17T16:54:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T16:32:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2026-02-17T12:04:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T05:28:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2026-02-05T22:18:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f163b252-b89e-4013-9c4f-4dd141d0ffbb","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T05:18:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 22:25:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8715910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8715910","identity":"rs-8715910","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}