Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis

doi:10.21203/rs.3.rs-7770089/v1

Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis

2025 · doi:10.21203/rs.3.rs-7770089/v1

preprint OA: closed

Full text JSON View at publisher

⚙ AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text ⓘ

This paper studies how to improve forecasting of water consumption by accounting for complex, seasonal, and non-stationary relationships with weather variables. The authors develop a seasonal-adaptive feature engineering framework that uses adaptive correlation thresholds, systematic temperature feature construction, stability checking, and analyses of delayed effects and interactions, including protocols for adapting to new regions; they report cross-regional experiments across five climate zones of Spain. They find reduced mean absolute error relative to baselines (13.8% lower), competitive performance versus foundation models (4.3–6.5% better), higher feature stability (29.9% more), and adaptation time of 1.8–4.2 days, with a final filtering step to increase operational reliability. A major caveat is that the work is presented as a preprint and has not been peer reviewed by a journal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract Forecasting water consumption is difficult because it depends on complex relation between demand and weather variables. Many traditional methods use fixed correlation and do not consider seasonal changes or non-stationary data. In this work, we propose a seasonal-adaptive feature engineering framework to solve these problems. The framework uses adaptive correlation thresholds, systematic construction of temperature features, and stability checking for better performance in different seasons. It also evaluates feature stability, applies adaptation protocols for new regions, and studies delayed effect and interaction of weather variables. Experiments show lower mean absolute error (13.8% less than baselines) and also competitive results with foundation models (4.3–6.5% better), but with much lower cost and higher feature stability (29.9% more). A last filtering step increases operational reliability. Cross-regional tests in five climate zones of Spain show adaptation time between 1.8 and 4.2 days. Results prove that adaptive feature engineering improves accuracy and robustness for water demand forecasting and helps utilities in resource planning.

Full text 13,834 characters · extracted from preprint-html · click to expand

Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis | 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 Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis Mohammadhossein Homaei, Mostafa Rezaee, Rubén Molano, Mar Avila, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7770089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Forecasting water consumption is difficult because it depends on complex relation between demand and weather variables. Many traditional methods use fixed correlation and do not consider seasonal changes or non-stationary data. In this work, we propose a seasonal-adaptive feature engineering framework to solve these problems. The framework uses adaptive correlation thresholds, systematic construction of temperature features, and stability checking for better performance in different seasons. It also evaluates feature stability, applies adaptation protocols for new regions, and studies delayed effect and interaction of weather variables. Experiments show lower mean absolute error (13.8% less than baselines) and also competitive results with foundation models (4.3–6.5% better), but with much lower cost and higher feature stability (29.9% more). A last filtering step increases operational reliability. Cross-regional tests in five climate zones of Spain show adaptation time between 1.8 and 4.2 days. Results prove that adaptive feature engineering improves accuracy and robustness for water demand forecasting and helps utilities in resource planning. Adaptive Algorithms Feature Engineering Hierarchical Features Machine Learning Meteorological Variables Seasonal Adaptation Water Consumption Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7770089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524203103,"identity":"379def16-c794-441b-9ab1-e4b158558afd","order_by":0,"name":"Mohammadhossein Homaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCSjJz8DcQKIWyQZG0rQwMBgcIFaL7uzmoxt+VFjkGR8/2CZ1c4eNPYNE7sHHPAxABg5gdudY2s2eMxLFZmcS26Rzz6QlNkjkJRvOYAAycGm5kWN2g7dNInHbAZCWtsMJDBI5ZhIfGIAMPFpu/v0nkbi5/yFYC9BhOeY/Ehj+43YYUMtt3gaJxA0SEFsYG4C2MHxgwB0aZjfS0m7LHJNInHHjYbN1bltaYhvPG2PJGQbJePySfOzmm5q6xP7+5IO3c9ts7PnZcww/81TY4XQYJmADkwbEaxgFo2AUjIJRgAkAZkBX84JGUA8AAAAASUVORK5CYII=","orcid":"","institution":"University of Extremadura","correspondingAuthor":true,"prefix":"","firstName":"Mohammadhossein","middleName":"","lastName":"Homaei","suffix":""},{"id":524203104,"identity":"91c76f37-360e-4cb3-acad-043ef8f52366","order_by":1,"name":"Mostafa Rezaee","email":"","orcid":"","institution":"Bowling Green State University","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Rezaee","suffix":""},{"id":524203105,"identity":"3c8fbb04-f2b6-4b1d-8c7e-f8c4ba1befa8","order_by":2,"name":"Rubén Molano","email":"","orcid":"","institution":"University of Extremadura","correspondingAuthor":false,"prefix":"","firstName":"Rubén","middleName":"","lastName":"Molano","suffix":""},{"id":524203106,"identity":"f2166e52-59c2-4f50-95a3-7b30363082bf","order_by":3,"name":"Mar Avila","email":"","orcid":"","institution":"University of Extremadura","correspondingAuthor":false,"prefix":"","firstName":"Mar","middleName":"","lastName":"Avila","suffix":""},{"id":524203107,"identity":"e7df6bca-c9bf-474c-9a88-e578939d4d35","order_by":4,"name":"Andres Caro","email":"","orcid":"","institution":"University of Extremadura","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Caro","suffix":""}],"badges":[],"createdAt":"2025-10-03 04:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7770089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7770089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92911613,"identity":"525c6b87-4e35-48e1-8c7b-120c082ce6a3","added_by":"auto","created_at":"2025-10-07 03:54:37","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7149,"visible":true,"origin":"","legend":"","description":"","filename":"f07db0102ef54c4a8359727c07f429af.json","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/6660c9341e89b9658781161b.json"},{"id":92911614,"identity":"671aa02c-b34d-4264-a0fa-4298e372737f","added_by":"auto","created_at":"2025-10-07 03:54:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44344,"visible":true,"origin":"","legend":"","description":"","filename":"CoverletterSeasonalSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/c9f9f00147cc1fc1c8d60615.pdf"},{"id":92911775,"identity":"4382cdf7-e86a-46f9-856b-568a0f746888","added_by":"auto","created_at":"2025-10-07 04:02:37","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208834,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/4b58f369ef977c7deaa6bcfa.png"},{"id":92912145,"identity":"f96cfe7f-5a7b-4668-b84f-76437aa9f203","added_by":"auto","created_at":"2025-10-07 04:10:37","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":599092,"visible":true,"origin":"","legend":"","description":"","filename":"SeasonalAdaptiveFeatureEngineeringforWaterConsumptionPredictionACrossRegionalStabilityAnalysisSpringer2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/ea28985888d2ae22dd241deb.pdf"},{"id":92911615,"identity":"0d9557da-34b0-4657-8609-bea4cdbb8f03","added_by":"auto","created_at":"2025-10-07 03:54:37","extension":"cls","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/72ad3844d109a3bfcf5725a0.cls"},{"id":92911616,"identity":"dddc510a-f982-4961-9124-a4f0e29f81bc","added_by":"auto","created_at":"2025-10-07 03:54:37","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173456,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/b1c8c51e330fbea3fc7649ca.png"},{"id":92911619,"identity":"bebeb5da-e316-4639-a33d-d51be2926c38","added_by":"auto","created_at":"2025-10-07 03:54:37","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163097,"visible":true,"origin":"","legend":"","description":"","filename":"f07db0102ef54c4a8359727c07f429af1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1/4862a4a7bb6506d882852060.xml"},{"id":96135781,"identity":"5c484880-c20c-47ab-9f0f-c06dbc4bffa7","added_by":"auto","created_at":"2025-11-18 03:53:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":574969,"visible":true,"origin":"","legend":"","description":"","filename":"SeasonalAdaptiveFeatureEngineeringforWaterConsumptionPredictionACrossRegionalStabilityAnalysisSpringer2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7770089/v1_covered_570742b3-fab5-452a-89d4-3409ccbc7bdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adaptive Algorithms, Feature Engineering, Hierarchical Features, Machine Learning, Meteorological Variables, Seasonal Adaptation, Water Consumption Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7770089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7770089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Forecasting water consumption is difficult because it depends on complex relation between demand and weather variables. Many traditional methods use fixed correlation and do not consider seasonal changes or non-stationary data. In this work, we propose a seasonal-adaptive feature engineering framework to solve these problems. The framework uses adaptive correlation thresholds, systematic construction of temperature features, and stability checking for better performance in different seasons. It also evaluates feature stability, applies adaptation protocols for new regions, and studies delayed effect and interaction of weather variables. Experiments show lower mean absolute error (13.8% less than baselines) and also competitive results with foundation models (4.3–6.5% better), but with much lower cost and higher feature stability (29.9% more). A last filtering step increases operational reliability. Cross-regional tests in five climate zones of Spain show adaptation time between 1.8 and 4.2 days. Results prove that adaptive feature engineering improves accuracy and robustness for water demand forecasting and helps utilities in resource planning.","manuscriptTitle":"Seasonal-Adaptive Feature Engineering for Water Consumption Prediction: A Cross-Regional Stability Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 03:54:32","doi":"10.21203/rs.3.rs-7770089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a38e0d8a-688c-48e7-a9a2-bb01c47876c6","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T03:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 03:54:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7770089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7770089","identity":"rs-7770089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: preprint-html ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc: last seen: 2026-05-20T01:45:00.602351+00:00