[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119994-en":3,"doc-seo-119994-105":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},119994,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Combining traditional hydrological models and machine learning for streamflow prediction","Traditional hydrological models underpin many hydrologic studies, offering credible representations of real-world processes. This research proposes a hybrid approach that integrates the Soil Moisture Accounting Procedure (SMAP) with XGBoost for streamflow prediction. For the Sobradinho watershed, rainfall forecasts from the North America Multi Model Ensemble (NMME) feed SMAP after NMME evaluation and bias correction via quantile mapping. SMAP is calibrated using PSO (1984–2010), evaluated on 2011–2022. XGBoost learns SMAP residuals from the previous 12 months and corrects streamflow forecasts, improving correlation and Nash-Sutcliffe performance, especially for low flows.","Revista Brasileira de Recursos Hídricos  \nBrazilian Journal of Water Resources  \nVersão On-line ISSN 2318-0331  \nRBRH, Porto Alegre, v. 29, e11, 2024  \nScientific/Technical Article  \n[https://doi.org/10.1590/2318-0331.292420230105](https://doi.org/10.1590/2318-0331.292420230105)  \nCombining traditional hydrological models and machine learning for streamflow  \nprediction  \nCombinando modelos hidrológicos tradicionais e aprendizado de máquina para previsão de vazão  \nAntonio Duarte Marcos Junior1 􀀬 , Cleiton da Silva Silveira2 􀀬 , José Micael Ferreira da Costa3 􀀬 &  \nSuellen Teixeira Nobre Gonçalves2 􀀬  \n1 Universidade Federal do Ceará, Redenção, CE, Brasil  \n2 Universidade Federal do Ceará, Fortaleza, CE, Brasil  \n3 Universidade Federal do Ceará, Acarape, CE, Brasil  \n[E-mails: duarte.jr105@gmail.com](E-mails: duarte.jr105@gmail.com) (ADMJ), [cleitonsilveira@ufc.br](cleitonsilveira@ufc.br) (CSS), [jmicaelcosta@gmail.com](jmicaelcosta@gmail.com) (JMFC), [suellen.nobre@gmail.com](suellen.nobre@gmail.com) (STNG)  \nReceived: September 25, 2023-Revised: December 30, 2023-Accepted: February 22, 2024  \nABSTRACT  \nTraditional hydrological models have been widely used in hydrologic studies, providing credible representations of reality. This paper introduces a hybrid model that combines the traditional hydrological model Soil Moisture Accounting Procedure (SMAP) with the machine learning algorithm XGBoost. Applied to the Sobradinho watershed in Brazil, the hybrid model aims to produce more precise streamflow forecasts within a three-month horizon. This study employs rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs of the SMAP to produce streamflow forecasts. The study evaluates NMME forecasts, corrects bias using quantile mapping, and calibrates the SMAP model for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO) . Model evaluation covers the period from 2011 to 2022. An XGBoost model predicts SMAP residuals based on the past 12 months, and the hybrid model combines SMAP’s streamflow forecast with XGBoost residuals. Notably, the hybrid model outperforms SMAP alone, showing improved correlation and Nash-Sutcliffe index values, especially during periods of lower streamflow. This research highlights the potential of integrating traditional hydrological models with machine learning for more accurate streamflow predictions.  \nKeywords: Hydrological models; Machine learning; Streamflow forecast; SMAP; XGBoost; Rainfall forecasting.  \nRESUMO  \nOs modelos hidrológicos tradicionais têm sido amplamente utilizados em estudos hidrológicos, fornecendo representações credíveis darealidade. Este artigo introduz um modelo híbrido que combina o modelo hidrológico tradicional Soil Moisture Accounting Procedure (SMAP) com o algoritmo de aprendizado de máquina XGBoost. Aplicado à bacia de Sobradinho no Brasil, o modelo híbrido tem como objetivo produzir previsões de vazão mais precisas em um horizonte de três meses. Este estudo utiliza previsões de chuvas do North America Multi Model Ensemble (NMME) como entradas do SMAP para produzir previsões de vazão. O estudo avalia as previsões do NMME, corrige viés usando mapeamento de quantis e calibra o modelo SMAP para a região de estudo de 1984 a 2010 usando a Otimização por Enxame de Partículas (PSO) . A avaliação do modelo abrange o período de 2011 a 2022. Um modelo XGBoost prevê os resíduos do SMAP com base nos últimos 12 meses, e o modelo híbrido combina a previsão de vazão do SMAP com os resíduos do XGBoost. Notavelmente, o modelo híbrido supera o SMAP sozinho, mostrando melhor correlação e valores do índice Nash-Sutcliffe, especialmente durante períodos de menor vazão. Esta pesquisa destaca o potencial da integração de modelos hidrológicos tradicionais com aprendizado de máquina para previsões de vazão mais precisas.  \nPalavras-chave: Modelos hidrológicos; Aprendizado de máquina; Previsão de vazão; SMAP; XGBoost; Previsão de chuvas.  \nThis is an Open A","cbCaipB9OkmmodUl","https://ap.wps.com/l/cbCaipB9OkmmodUl","pdf",3081888,1,17,"English","en",105,"# Abstract\n## Methodology\n## Calibration and Evaluation\n## Hybrid Model Performance","[{\"question\":\"What hybrid model is proposed for streamflow forecasting?\",\"answer\":\"The study combines SMAP (a traditional hydrological model) with XGBoost. XGBoost predicts SMAP residuals and the hybrid output merges SMAP forecasts with these learned corrections.\"},{\"question\":\"How are rainfall forecasts prepared for SMAP?\",\"answer\":\"The approach uses rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs to SMAP. NMME forecasts are evaluated and their bias is corrected using quantile mapping.\"},{\"question\":\"How are SMAP and the hybrid model calibrated and assessed?\",\"answer\":\"SMAP is calibrated for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO). Model evaluation is performed for 2011 to 2022, comparing forecast skill with metrics including correlation and the Nash-Sutcliffe index.\"}]","Combining traditional hydrological models and machine learning for streamflow prediction | PDF",1785727568,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"combining-traditional-hydrological-models-and-machine-learning-for-streamflow-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/combining-traditional-hydrological-models-and-machine-learning-for-streamflow-prediction/119994/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What hybrid model is proposed for streamflow forecasting?","Question",{"text":75,"@type":76},"The study combines SMAP (a traditional hydrological model) with XGBoost. XGBoost predicts SMAP residuals and the hybrid output merges SMAP forecasts with these learned corrections.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are rainfall forecasts prepared for SMAP?",{"text":80,"@type":76},"The approach uses rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs to SMAP. NMME forecasts are evaluated and their bias is corrected using quantile mapping.",{"name":82,"@type":73,"acceptedAnswer":83},"How are SMAP and the hybrid model calibrated and assessed?",{"text":84,"@type":76},"SMAP is calibrated for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO). Model evaluation is performed for 2011 to 2022, comparing forecast skill with metrics including correlation and the Nash-Sutcliffe index.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]