Analysis of Rainfall-runoff Neuron Input Model with Artificial Neural Network for Simulation for Availability of Discharge at Bah Bolon Watershed

Setiono,; Hadiani, Rintis

doi:10.1016/j.proeng.2015.11.022

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…As RNAs desenvolvidas para as seções de monitoramento de vazão da bacia do rio Piracicaba apresentaram desempenho adequado e semelhante aos obtidos por Aichouri et al (2015), Elsafi (2014), Mehr et al (2015), Okkan et al (2012), Setiono (2015), Tayyab et al (2016) e Tongal et al (2013), quando da utilização de RNAs com o mesmo objetivo, em bacias hidrográficas de diferentes países.…”

Section: Estação Carrapato (56640000)unclassified

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Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Uliana

Silva²,

Moreira

et al. 2018

R_I

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Palavras-Chave: modelo empírico inteligência artificial, modelagem hidrológica. ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, D. R.; PEREIRA, S. B.; ALMEIDA, F. T. ARTIFICIAL NEURAL NETWORKS FOR DAILY FLOW ESTIMATES IN THE PIRACICABA RIVER BASIN 2 ABSTRACTArtificial neural networks (ANNs) have been used alternatively in hydrologic modeling to estimate accurately watercourse flows based on hydrometeorological data. This study developed artificial neural networks to estimate daily flows in Piracicaba river basin, in Minas Gerais state (Brazil). For this, we used three runoff-monitoring sections of the Piracicaba river

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Section: Estação Carrapato (56640000)unclassified

“…Entre os modelos empíricos, as Redes Neurais Artificiais (RNAs) apresentam resultados promissores para a estimativa das vazões de cursos de água, como demonstrado por Aichouri et al (2015), Elsafi (2014), Meng et al (2015), Oliveira et al (2013), Sattari;Apaydin;Ozturk (2012) e Setiono (2015).…”

Section: Introductionunclassified

Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Uliana

Silva²,

Moreira

et al. 2018

R_I

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“…At any given spatial and temporal variations in explicit and implicit variables of watershed and precipitation characteristics, the relationship between rainfall and runoff is nonlinear and extremely complex (Kumar et al, 2019a(Kumar et al, , 2019bRezaie-Balf et al, 2017;Wu & Chau, 2011). The complex process of transformation of rainfall to runoff can be simulated through hydrologic models (Hadiani, 2015). The runoff simulation modelling methods can be broadly categorized into categories of theory-driven (conceptual) and datadriven (black box) approaches (Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

Singh

Kumar

Jaiswal

et al. 2021

Environ Dev Sustain

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Runoff estimation is of immense importance in hydrological analysis for water resource planning and management. The developing countries cannot afford to establish a large number of gauging sites due to huge initial and operating expenditures. Hydrological modelling is an alternative solution to simulate the catchment response to extreme events under climate change for taking preventive measures. The hydrological models have their own leads and constraints, so because of limited hydrological data availability of the catchment, wavelet neural network (WNN), artificial neural network, adaptive neuro-fuzzy inference system, and Mike-11 Nedbor Afstromnings models were used in this study. These models were calibrated and validated using daily rainfall and runoff observations taken at Hamp Pandariya gauging station on Hamp river in the Chhattisgarh state of India. A comparative study of these models was carried out to investigate their performance, efficiency, and suitability for daily runoff simulation in Hamp Pandariya catchment and found suitable in simulating the hydrological response of the catchment and predicting runoff with a high degree of accuracy. The performance of these models was evaluated and compared with the aid of multiple goodness of fit criteria including coefficient of determinations (r 2 ), Nash-Sutcliffe model efficiency index (NS), root mean square error, and water balance for model calibration and validation. These parameters indicated good agreement between observed and simulated runoff in terms of time to peak, discharge rate, daily and accumulated runoff volume, and shape of the hydrograph. The WNN was found the most appropriate model for future application due to Nash-Sutcliffe efficiency (NS) of 97% and 98% in calibration and validation, respectively, and the coefficient of determination as 99% both in calibration and validation.

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“…Artificial Neural Network (ANN) method was used by [10] to determine the potential of the available discharge in the long term for the purpose of Micro Hydro Power (MHP).The modeling of rainfall-runoff in the watershed of Bolon in Simalungun district of North Sumatra Province. The software was developed with Scilab mathematical open source software based on ANN algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Smart Phone Camera Calibration Approaches Using the Digital Close-Range Photogrammetry Technique

Fawzy¹

2020

International Journal of Civil Engineering and Technology (Ijciet)

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Artificial Neural Network (ANN) was applied to predict the long-term monthly rainfall for selected stations in Jordan valley. Meteorological data measured by Jordan meteorological department in the period between 1975 and 2005 were used. Three neurons which receive input signals of total monthly rainfall for three stations were used in the input layer of the network. One neuron, which produces corresponding output signals of the total monthly rainfall, to one station, is utilized in the output layer of the network. Finally, the values determined by the artificial neural network model were compared with the actual data. Errors obtained in this model are well within acceptable limits.

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Analysis of Rainfall-runoff Neuron Input Model with Artificial Neural Network for Simulation for Availability of Discharge at Bah Bolon Watershed

Cited by 11 publications

References 4 publications

Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

A Comparison of Smart Phone Camera Calibration Approaches Using the Digital Close-Range Photogrammetry Technique

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