Rainfall Runoff Analysis using Artificial Neural Network

Chakravarti, Ankit; Joshi, Nitin; Panjiar, Himanshu

doi:10.17485/ijst/2015/v8i14/54370

Cited by 27 publications

(14 citation statements)

References 20 publications

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“…Other researchers used artificial neural network models to estimate of rainfall -runoff relation, and the results showed the high reliability of the artificial models according to various scenarios [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Slieman

Козлов

2021

E3S Web Conf.

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Runoff is one of the most important components of the hydrological cycle, and having complete series of runoff data is essential for any hydrological modelling process. This study aims to estimate the runoff at Al-Jawadiyah hydrometric station using artificial neural networks. This study used only the runoff data at Al-Jawadiyah station in addition to the runoff values measured at Al-Amiri station on the Syrian-Lebanese border. Many experiments were conducted and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons and the training algorithms until the best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values, and the network (2:12:1) was adopted in the process of filling the gaps in the runoff time series at Al-Jawadiyah station during the study period. This study recommends working on preparing complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area.

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Section: Introductionmentioning

confidence: 99%

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Slieman

Козлов

2021

E3S Web Conf.

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“…However, SWAT was more successful in better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases [4]. Other researchers used artificial neural network models to estimate rainfall-runoff relations, and the results showed the high reliability of the artificial models according to various scenarios [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study Between Artificial Neural Networks and Fuzzy Inference System for Estimation and Filling of Missing Runoff Data at Al-Jawadiyah Station

Slieman

Козлов

2021

E3S Web Conf.

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Runoff is one of the most important components of the hydrological cycle, and having complete series of runoff data is essential for any hydrological modelling process. This study compares artificial neural networks and fuzzy inference systems for estimation of runoff data at the Al-Jawadiyah hydrometric station. This study used only the runoff data at Al-Jawadiyah station in addition to the runoff values measured at Al-Amiri station on the Syrian-Lebanese border. Many experiments were conducted, and a very large number of artificial neural networks were trained with changing the number of hidden layers, the number of neurons, and the training algorithms until the best network was reached according to the regression criteria and the root mean of the error squares between the measured values and the predicted values. Also, many fuzzy inference models have been prepared, changing the number and type of membership functions until the most accurate model has been reached. The results showed the high reliability of both the artificial neural network models and the fuzzy inference models in estimating runoff in the study area, and the comparison between the results showed the great convergence of the two models with a slight preference for the fuzzy models. This study recommends using the rest of the artificial intelligence models and comparing them to arrive at the most accurate model. This helps prepare a complete series of hydrological and climatic measurements that form a basis for preparing an accurate hydrological model for the study area.

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“…https://www.indjst.org/ In hydrology, the research work is to develop the model to find out the future basin discharge which is a safe and economical tool for hydrological engineering design (6)(7)(8) . The water resources management using ANNs which involves the estimation of Rainfall-Runoff event, river discharge forecasting, Climate Change, river inflow modeling and estimation of groundwater, etc (9)(10)(11) . The ANNs were developed to an observed daily runoff as a function of daily rainfall, temperature, cloud cover, potential evapotranspiration, and relative humidity.…”

Section: Introductionmentioning

confidence: 99%

A rainfall-runoff model using artificial neural networks for the district of Bankura in a time of climate change

Dey¹

2020

IJST

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Objectives: The main objective of this research is to determine the Hydrological (Rainfall-Runoff) model by using Artificial Neural Networks (ANNs) for the study area. Methods: The ANNs model was applied to the relative impact of different climatic variables such as Rainfall, Temperature, Cloud Cover, Potential Evapotranspiration, and Relative Humidity for the district of Bankura located on Lower Gangetic Plain (Zone no-III) in India. This study has also developed runoff hydrograph using various Slope, Rainfall Intensity, and Roughness over the catchment. The researcher has collected the Real-Time data series of 116 years (1901-2016) for the six meteorological stations of district Bankura from India Meteorological Department, Pune. For estimating runoff values, the study has been used Kothyari and Garde equation in which the most important factor i.e. the Vegetal Cover Factor (Fv) was considered. For developing the ANNs model, the available data were separated as 70% for training, 15% for testing, and 15% for validation. Findings: The Predicted values using ANNs model are more useful for better estimation of water resources management than previous researches. The model performance was with better efficiency (Nash-Sutcliffe Efficiency) and it was greater than 97%. Novelty: First time, this research established the Hydrological (Rainfall-Runoff) model by using Artificial Neural Networks (ANNs) for the study area.

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Rainfall Runoff Analysis using Artificial Neural Network

Cited by 27 publications

References 20 publications

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

Estimation and filling of missing runoff data at Al-Jawadiyah station using artificial neural networks

A Comparative Study Between Artificial Neural Networks and Fuzzy Inference System for Estimation and Filling of Missing Runoff Data at Al-Jawadiyah Station

A rainfall-runoff model using artificial neural networks for the district of Bankura in a time of climate change

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