Deriving reservoir operating rule based on multiple evolutionary search, data mining and machine learning methods

Deriving reservoir operating rule based on multiple evolutionary search, data mining and machine learning methods | 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 Deriving reservoir operating rule based on multiple evolutionary search, data mining and machine learning methods Zijia Mi, Xiang Li, Xianzhi Wang, Juan Bao, Tianchen Li, Jiahua Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7253444/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Reservoir operating rules are essential guidelines for effective reservoir management and the realization of integrated benefits. Although optimization-based reservoir operation methods have advanced rapidly in recent years, a significant gap between theory and practice has hindered their direct application in real-world operations. To address this issue, this study proposes an integrated methodology that combines evolutionary search, data mining, and machine learning to derive reservoir operating rules. The proposed framework consists of three components: (1) the formulation of a maximum hydroenergy production optimization model solved using Genetic Algorithms (GA), Differential Evolution (DE), and Pattern Search (PS); (2) the identification of key decision factors through Grey Relational Analysis and Kendall’s tau rank correlation coefficient; (3) the derivation and performance evaluation of operating rules using traditional methods (MLR, SGM), classical machine learning methods (ANN, SVM), and emerging machine learning methods (GRU, CatBoost). This methodology is applied to the Ma’erdang Reservoir in the source region of the Yellow River. Characteristic reservoir param and ten-day runoff data from the Jungong station (1980 ~ 2023) are utilized for multi-scenario simulation and comparative analysis. The results indicate that: 1) Without consideration of guaranteed output, all three evolutionary search algorithms yield an average annual energy production of 7.48 billion kWh, and with consideration of guaranteed output, the long-term average generation decreases to 7.46, 7.45, and 7.31 billion kWh for Genetic Algorithm (GA), Differential Evolution (DE), and Pattern Search (PS), respectively. 2) The factor selection results indicate that, in both without consideration of guaranteed output and with consideration of guaranteed output scenarios, six key factors were consistently identified, covering aspects such as the month of the time period, inflow, outflow, and forebay water level. This method reduced the dimensionality of multi-dimensional decision factors by 60% while maintaining over 95% of the decision relevance. 3) Dividing the hydrological time series into a training set (1980 ~ 2014) and a test set (2015 ~ 2023) allowed comparison of the fitting and prediction performance of machine learning methods (ANN, SVM, GRU, CatBoost) and traditional methods (MLR, SGM). Taking the multi-year average energy production obtained from direct optimization as the benchmark, operating rules derived using emerging machine learning models (GRU, CatBoost) achieved up to 99% of the benchmark, those derived from classical machine learning models (ANN, SVM) reached 96%, and those from traditional methods (MLR, SGM) attained 91%. Reservoir operation Operating rule Evolutionary search algorithm Data mining Machine learning Full Text Supplementary Files Highlights.docx coverletter.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 05 Mar, 2026 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 30 Jul, 2025 First submitted to journal 30 Jul, 2025 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-7253444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500595764,"identity":"7ac64d22-df4c-4171-b7b9-f9b61e1de955","order_by":0,"name":"Zijia Mi","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Zijia","middleName":"","lastName":"Mi","suffix":""},{"id":500595765,"identity":"8aa0ff7c-68ab-4218-88a1-92dc5206397e","order_by":1,"name":"Xiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPmYGhgNAWo6NgYdILWxQLcYkaIHSiQ3Ea2HnMTzwc0dtep9E7rEPDDU20UQ4jC3hYO+Z47ltEnnJMxiOpeU2ENbCfOAAb9ux3DbpHGMGxobDxGhhbDj4t+1YOhsJWpgPHOZtq0kgRQtbwmHZtgOGbfJvjBkSiPELP/8Z449v2+rk5XvOGDN8qLEhrAUKDkOoBCKVg0AdCWpHwSgYBaNgxAEAv5s2+o+Qj1wAAAAASUVORK5CYII=","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":true,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":500595766,"identity":"eb892df5-2c8d-448a-b4cf-69797cf240a4","order_by":2,"name":"Xianzhi Wang","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Xianzhi","middleName":"","lastName":"Wang","suffix":""},{"id":500595767,"identity":"53b45e2b-7bf3-48fc-989c-08ac9bc13ef6","order_by":3,"name":"Juan Bao","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Bao","suffix":""},{"id":500595768,"identity":"eb11048a-ff9d-468a-aa5c-2bc7465880af","order_by":4,"name":"Tianchen Li","email":"","orcid":"","institution":"Guoneng Qinghai Yellow River Ma'erdang Hydropower Development Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Tianchen","middleName":"","lastName":"Li","suffix":""},{"id":500595769,"identity":"584153b0-429e-44d5-959f-6e0b42eee439","order_by":5,"name":"Jiahua Wei","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Jiahua","middleName":"","lastName":"Wei","suffix":""},{"id":500595770,"identity":"75737cca-bef3-4dfa-bc5e-449cef67dd5d","order_by":6,"name":"Xiangpeng Mu","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Xiangpeng","middleName":"","lastName":"Mu","suffix":""},{"id":500595771,"identity":"0f13da22-2d6b-437b-b5b1-5f3f5e2c1f9b","order_by":7,"name":"Baoligao Baiyin","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Baoligao","middleName":"","lastName":"Baiyin","suffix":""},{"id":500595772,"identity":"0104799c-6bd3-4050-82b0-c55fb7417035","order_by":8,"name":"Fengran Xu","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"Fengran","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-07-30 13:29:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7253444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7253444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89631616,"identity":"0f150396-139e-48db-9396-2840893807f2","added_by":"auto","created_at":"2025-08-22 06:50:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1519701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7253444/v1_covered_9ae9fab3-fc23-4dfe-b9c5-300d98fc5da9.pdf"},{"id":89629023,"identity":"519669a6-9903-4f22-a119-67d765a6bc4d","added_by":"auto","created_at":"2025-08-22 06:26:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14052,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-7253444/v1/27a632ad9d28ca61448fd011.docx"},{"id":89629025,"identity":"789a92c2-499a-4418-b3d0-28063669fc67","added_by":"auto","created_at":"2025-08-22 06:26:25","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14821,"visible":true,"origin":"","legend":"","description":"","filename":"coverletter.docx","url":"https://assets-eu.researchsquare.com/files/rs-7253444/v1/455f7b4d6cc22f54910e4f89.docx"}],"financialInterests":"","formattedTitle":"Deriving reservoir operating rule based on multiple evolutionary search, data mining and machine learning methods","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Reservoir operation, Operating rule, Evolutionary search algorithm, Data mining, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7253444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7253444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReservoir operating rules are essential guidelines for effective reservoir management and the realization of integrated benefits. Although optimization-based reservoir operation methods have advanced rapidly in recent years, a significant gap between theory and practice has hindered their direct application in real-world operations. To address this issue, this study proposes an integrated methodology that combines evolutionary search, data mining, and machine learning to derive reservoir operating rules. The proposed framework consists of three components: (1) the formulation of a maximum hydroenergy production optimization model solved using Genetic Algorithms (GA), Differential Evolution (DE), and Pattern Search (PS); (2) the identification of key decision factors through Grey Relational Analysis and Kendall\u0026rsquo;s tau rank correlation coefficient; (3) the derivation and performance evaluation of operating rules using traditional methods (MLR, SGM), classical machine learning methods (ANN, SVM), and emerging machine learning methods (GRU, CatBoost). This methodology is applied to the Ma\u0026rsquo;erdang Reservoir in the source region of the Yellow River. Characteristic reservoir param and ten-day runoff data from the Jungong station (1980\u0026thinsp;~\u0026thinsp;2023) are utilized for multi-scenario simulation and comparative analysis. The results indicate that: 1) Without consideration of guaranteed output, all three evolutionary search algorithms yield an average annual energy production of 7.48\u0026nbsp;billion kWh, and with consideration of guaranteed output, the long-term average generation decreases to 7.46, 7.45, and 7.31\u0026nbsp;billion kWh for Genetic Algorithm (GA), Differential Evolution (DE), and Pattern Search (PS), respectively. 2) The factor selection results indicate that, in both without consideration of guaranteed output and with consideration of guaranteed output scenarios, six key factors were consistently identified, covering aspects such as the month of the time period, inflow, outflow, and forebay water level. This method reduced the dimensionality of multi-dimensional decision factors by 60% while maintaining over 95% of the decision relevance. 3) Dividing the hydrological time series into a training set (1980\u0026thinsp;~\u0026thinsp;2014) and a test set (2015\u0026thinsp;~\u0026thinsp;2023) allowed comparison of the fitting and prediction performance of machine learning methods (ANN, SVM, GRU, CatBoost) and traditional methods (MLR, SGM). Taking the multi-year average energy production obtained from direct optimization as the benchmark, operating rules derived using emerging machine learning models (GRU, CatBoost) achieved up to 99% of the benchmark, those derived from classical machine learning models (ANN, SVM) reached 96%, and those from traditional methods (MLR, SGM) attained 91%.\u003c/p\u003e","manuscriptTitle":"Deriving reservoir operating rule based on multiple evolutionary search, data mining and machine learning methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 06:26:20","doi":"10.21203/rs.3.rs-7253444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"Water Resources Management","date":"2026-03-05T07:18:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-14T16:01:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T14:32:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T02:46:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2025-07-30T22:33:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"34ee5b57-b06e-4d23-87b1-cf6eb8f53b42","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-22T06:26:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 06:26:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7253444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7253444","identity":"rs-7253444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}