Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

Jothiprakash, V.; Magar, Rajendra

doi:10.1016/j.jhydrol.2012.04.045

Cited by 82 publications

(28 citation statements)

References 61 publications

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“…Over the past years, machine learning approaches have been effectively applied for modeling nonlinear hydrologic systems. Especially, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) have been acknowledged as successful tools for modeling complex hydrologic systems (Noori et al 2011;Othman and Naseri 2011;Ismail et al 2012;Jothiprakash and Magar 2012;Kim et al 2013;Seo et al 2013aSeo et al , 2013bSeo et al , 2013cSudheer et al 2014).…”

Section: Introductionmentioning

confidence: 99%

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Seo

Kim

Kisi

et al. 2016

Water Resour Manage

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This study develops and applies three hybrid models, including wavelet packetartificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system (WPANFIS) and wavelet packet-support vector machine (WPSVM), combining wavelet packet decomposition (WPD) and machine learning models, ANN, ANFIS and SVM models, for forecasting daily river stage and evaluates their performance. The WPANN, WPANFIS and WPSVM models using inputs decomposed by the WPD are found to produce higher efficiency based on statistical performance criteria than the ANN, ANFIS and SVM models using original inputs. Performance evaluation for various mother wavelets indicates that the model Water Resour Manage performance is dependent on mother wavelets and the WPD using Symmlet-10 and Coiflet-18 is more effective to enhance the efficiency of the conventional machine learning models than other mother wavelets. It is found that the WPANFIS model outperforms the WPANN and WPSVM models, and the WPANFIS14-coif18 model produces the best performance among all other models in terms of model efficiency. Therefore, the WPD can significantly enhance the accuracy of the conventional machine learning models, and the conjunction of the WPD and machine learning models can be an effective tool for forecasting daily river stage accurately .

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Section: Introductionmentioning

confidence: 99%

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Seo

Kim

Kisi

et al. 2016

Water Resour Manage

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“…A common aspect of GP based modelling that all these studies reported is the fact that the GP modelling resulted in fairly simpler models which could be easily interpreted for the physical significance of the input variables in making a prediction. Jyothiprakash and Magar (2012) [12] performed a comparative study of reservoir inflow models developed using ANN, ANFIS and linear GP for lumped and distributed data. The study reported superior performance of GP models over ANN and ANFIS models.…”

Section: Genetic Programming As a Modelling Toolmentioning

confidence: 99%

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

Janardhanan¹,

Datta²

2012

Genetic Programming - New Approaches and Successful Applications

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“…Sharma and Chowdhury () reviewed static and dynamic ensemble methods in probabilistic reservoir system forecasting models to reduce structural errors. Jothiprakash and Magar () predicted daily and hourly intermittent rainfall and reservoir inflow using ANN, ANFIS, and linear genetic programming (GLP). Valipour et al .…”

Section: Introductionmentioning

confidence: 99%

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Zhao

Minsker

Salas

et al. 2017

J American Water Resour Assoc

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Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real-time decisions given that flood conditions change rapidly. This study's objective is to build real-time data-driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser-based Web application, enabling ease of use by both technical and nontechnical personnel.(KEY TERMS: flooding; data-driven model services; AzureML; reservoir inflow.)

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Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

Cited by 82 publications

References 61 publications

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

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