[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121749-en":3,"doc-seo-121749-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121749,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting","Reservoirs regulate and store river flows, making reliable outflow prediction essential for early warning systems and effective water management as climate change intensifies uncertainty. This study applies three machine learning approaches—Random Forest, Support Vector Machine, and artificial neural network—to forecast one-day-ahead outflow for eight dams in the Miño-Sil Hydrographic Confederation using current-day input variables. Results indicate strong performance under normal conditions, with the ANN providing the best models for five reservoirs.","water   \nArticle  \nAssessment of Different Machine Learning Methods for Reservoir Outflow Forecasting  \nAnton Soria-Lopez, Carlos Sobrido-Pouso, Juan C. Mejuto  and Gonzalo Astray *  \nCitation: Soria-Lopez, A.;  \nSobrido-Pouso, C.; Mejuto, J.C.; Astray, G. Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting. Water 2023, 15, 3380. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/w15193380](10.3390/w15193380)  \nAcademic Editors: Jianjun Ni and Achim A. Beylich  \nReceived: 31 July 2023  \nRevised: 22 September 2023  \nAccepted: 23 September 2023  \nPublished: 27 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nUniversidade de Vigo, Departamento de Qu½mica F½sica, Facultade de Ciencias, 32004 Ourense, Spain;  \n[anton.soria@uvigo.gal](anton.soria@uvigo.gal) (A.S.-L.); [csobrido@alumnos.uvigo.es](csobrido@alumnos.uvigo.es) (C.S.-P.); [xmejuto@uvigo.es](xmejuto@uvigo.es) (J.C.M.)  \n* Correspondence: gastray@uvigo.es  \nAbstract: Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflowin normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.  \nKeywords: reservoir; outflow; machine learning; random forest; support vector machine; artificial neural network; prediction  \n1. Introduction  \nReservoirs can be defined as high, open-air storage areas formed from the construction of structures known as dams, capable of retaining and controlling the water flow [1] . Dams have been built since the first civilizations [2] . The uses of these bodies of water are very numerous: hydroelectric energy production; flood control; industrial and urban water supply; and irrigation systems, among others [3] . According to Hao et al. (2023), the main source of renewable energy in the world is hydropower [4], and, according to Gemechu and Kumar (2022) [5], it contributed around 16% of the total electricity supply in 2018 [6] . However, criticism of the construction of dams has increased considerably in recent decades due to their adverse social and environmental impacts [7] . In fact, dams lead to a loss of longitudinal connectivity of rivers [8] . According to Grill et al. (2019), only 37% of the rivers longer than 1000 km present in the world flow freely throughout their entire length [9] . For this reason, and according to Garc½a-Feal et al. (2022) [1], a crucial aspect for adequate water management and to resolve the problems caused by these infrastructuresin relation to flow variability is the coordination between reservoirs [10–13] aided by more recent research [14,15] . However, there are aspects that can lead to the prevention of this coordination because (i) numerous rivers flow through different countries with different regulations,(ii) most of the dams are controlled by private businesses with different rules and (iii) the proper operation of the dams depends on natural factors, ","cbCaiifoevZsLwZ5","https://ap.wps.com/l/cbCaiifoevZsLwZ5","pdf",6875065,1,21,"English","en",105,"# Abstract\n# Introduction\n## Reservoirs and water management\n## Dam impacts and flow coordination\n## Flood protection and climate-driven risks\n## Motivation for outflow forecasting","[{\"question\":\"Why is reservoir outflow forecasting important?\",\"answer\":\"Reservoir outflow prediction supports early warning systems and adequate water management, especially under climate change–driven variability.\"},{\"question\":\"Which machine learning methods are compared in the study?\",\"answer\":\"The study compares Random Forest (RF), Support Vector Machine (SVM), and an artificial neural network (ANN) for one-day-ahead forecasting.\"},{\"question\":\"How many dams and reservoirs are used for evaluation?\",\"answer\":\"The models are assessed using eight different dams, and the ANN provides the best model in five reservoirs under normal conditions.\"}]","Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting | 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is reservoir outflow forecasting important?","Question",{"text":75,"@type":76},"Reservoir outflow prediction supports early warning systems and adequate water management, especially under climate change–driven variability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are compared in the study?",{"text":80,"@type":76},"The study compares Random Forest (RF), Support Vector Machine (SVM), and an artificial neural network (ANN) for one-day-ahead forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"How many dams and reservoirs are used for evaluation?",{"text":84,"@type":76},"The models are assessed using eight different dams, and the ANN provides the best model in five reservoirs under normal 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