A self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed

Vista sencilla de ítem
dc.contributor.authorMorales, Yerel
dc.contributor.authorQuerales, Marvin
dc.contributor.authorRosas, Harvey
dc.contributor.authorAllende-Cid, Hector
dc.contributor.authorSalas, Rodrigo
dc.date.accessioned2022-11-30T02:46:38Z
dc.date.available2022-11-30T02:46:38Z
dc.date.issued2021
dc.description.abstractModeling the relationship between rainfall and runoff is an important issue in hydrology, but it is a complicated task because both the high levels of complexity in which both processes are embedded and the associated uncertainty, affect the forecasting. Neuro-fuzzy models have emerged as a useful approach, given the ability of neural networks to optimize parameters in a fuzzy system. In this work a Self-Identification Neuro-Fuzzy Inference Model (SINFIM) for modeling the relationship between rainfall and runoff on a Chilean watershed is proposed to reduce the uncertainty of selecting both the rainfall and runoff lags and the number of membership functions required in a fuzzy system. The data comes from the Diguillín river located in Ñuble region and average daily runoff and average daily rainfall recorded from years 2000 to 2018, according to the Chilean directorate of water resources (DGA). In addition, we worked with the Colorado River basin, located in the Maule region, to validate the method developed. The experimental results showed a good adjustment using the last 3 years as validation set, further improvement was achieved using only the last year was used as validation test, obtaining 84% of and Kling Gupta Efficiency, higher than other forecasting models such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial neural networks (ANN), and Long Short-Term Memory (LSTM) approach. In addition, Nash-Sutcliffe efficiency and percent BIAS indicate the method is a promising model. On the other hand, even better results were obtained in the validation basin, whose adjustment was 94% and an efficiency of 97%. Therefore, the proposed model is a solid alternative to forecast the runoff in a given watershed, obtaining good performance measurements, managing to predict both the low and peak runoff values from rainfall events, avoiding the requirement to determine a priori the lags of time series and the number of fuzzy rules.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameMorales_Sel2021.pdf
dc.identifier.doihttps://doi.org/10.1016/j.jhydrol.2020.125910
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7445
dc.languageen
dc.publisherElsevier
dc.sourceJournal of Hydrology
dc.subjectRAINFALL-RUNOFF MODELINGen_ES
dc.subjectNEURO-FUZZY MODELen_ES
dc.subjectSINFIM FRAMEWORKen_ES
dc.titleA self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Civil
uv.notageneralNo disponible para descarga

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Morales_Sel2021_noaccesible_.pdf
Tamaño:
3.75 MB
Formato:
Adobe Portable Document Format
Descripción:
Descargar

Colecciones

Artículos investigadores UV