Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks

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Journal of Water Resource and Protection

ISSN Print: 1945-3094
ISSN Online: 1945-3108
www.scirp.org/Journal/jwarp
E-mail: jwarp@scirp.org

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"Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks"
written by Avinash AGARWAL, R. K. RAI, Alka UPADHYAY,
published by Journal of Water Resource and Protection, Vol.1 No.5, 2009

has been cited by the following article(s):

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[4]	Development of real-time flood forecast model for vamsadhara river through hydrological approach
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[6]	Analysis of Watershed Characteristics and Spatial Soil Loss Estimation: A case Fincha Watershed, Western Ethiopia
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[7]	Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artifilacial intelligence algorithms
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[8]	Sediment Transport Modelling in an alluvial river with Artificial Neural Network
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[9]	Application of soft-computing techniques in forecasting sediment load and concentration
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[10]	Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods
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[15]	RUNOFF AND SEDIMENT YIELD MODELING USING ARTIFICIAL NEURAL NETWORK: CASE OF MUGER WATERSHED, ABBAY RIVER BASIN, ETHIOPIA
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[16]	Prediction of Water Discharge and Sediment in Teak Forested Areausing Artificial Neural Network Model
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[19]	Jurnal Penelitian Kehutanan Wallacea
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[20]	Prediction of water discharge and sediment in teak forested area using artificial neural network model
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[25]	MLP AND SMN BASED ARTIFICIAL NEURAL NETWORK MODELS WITH LINEAR AND NON-LINEAR INPUT FOR SEDIMENT YIELD PREDICTION …
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[26]	Comparison of Data-driven Models in Predicting Suspended Sediment Load of a Catchment in the Lower Mekong Basin
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[29]	Sediment yield modelling of Kal river in Maharashtra using artificial neural network model
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[34]	Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression
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[35]	Stochastic modelling of rainfall and runoff phenomenon: a time series approach review
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[37]	Using artificial neural network to estimate sediment load in Ungauged catchments of the Tonle Sap River Basin, Cambodia
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[38]	Development of Hydrologic Forecasting Model Connected with Real Time Watershed and River Monitoring System
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[39]	GIS Based Geomorphic Response Models for Prediction of Runoff and Sediment Yields from Small Watersheds of Tapi Basin of Maharashtra State
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[41]	Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India
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[42]	River suspended sediment prediction using various multilayer perceptron neural network training algorithms—a case study in Malaysia
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[44]	Radionuclides Migration Modelling Using Artificial Neural Networks and Parallel Computing
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[45]	Prediction of Runoff and Sediment Yield Using AnnAGNPS Model: Case of Erer-Guda Catchment, East Hararghe, Ethiopia
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[50]	Prédiction De Débit Solide Dans La Période De Crue Par l'Utilisation Des Réseaux De Neurones Artificiels: Étude De Cas De l'Oued Mekerra-Sidi Bel Abbes …


[51]	A Hybrid Swat-Ann Model Approach for Enhanced Analysis of Climate Change Impacts on Sediment Yield in an Eastern Himalayan Sub-Watershed of Brahmaputra

[1]	Prioritization of sediment yield at sub-watershed level using swat model in Finca’aa watershed, Abay Basin, Ethiopia Journal of Sedimentary Environments, 2022 DOI:10.1007/s43217-022-00099-2

[2]	Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models Mathematics, 2022 DOI:10.3390/math10224263

[3]	Artificial intelligence for suspended sediment load prediction: a review Environmental Earth Sciences, 2021 DOI:10.1007/s12665-021-09625-3

[4]	Artificial intelligence for suspended sediment load prediction: a review Environmental Earth Sciences, 2021 DOI:10.1007/s12665-021-09625-3

[5]	Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020 DOI:10.1063/5.0008195

[6]	Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms International Journal of Sediment Research, 2020 DOI:10.1016/j.ijsrc.2020.03.018

[7]	Sediment transport modelling in an alluvial river with artificial neural network Journal of Hydrology, 2020 DOI:10.1016/j.jhydrol.2020.125056

[8]	Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020 DOI:10.1063/5.0008195

[9]	Application of soft-computing techniques in forecasting sediment load and concentration Hydrological Sciences Journal, 2020 DOI:10.1080/02626667.2020.1790565

[10]	The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction CATENA, 2019 DOI:10.1016/j.catena.2018.10.047

[11]	Interdisciplinary application of numerical and machine-learning-based models to predict half-hourly suspended sediment concentrations during typhoons Journal of Hydrology, 2019 DOI:10.1016/j.jhydrol.2019.04.001

[12]	Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India Modeling Earth Systems and Environment, 2017 DOI:10.1007/s40808-017-0323-y

[13]	Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier Hydrological Processes, 2016 DOI:10.1002/hyp.10723

[14]	Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier Hydrological Processes, 2016 DOI:10.1002/hyp.10723

[15]	Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression Water Resources Management, 2014 DOI:10.1007/s11269-014-0590-6

[16]	The river runoff forecast based on the modeling of time series Russian Meteorology and Hydrology, 2014 DOI:10.3103/S1068373914110053

[17]	Development of Hydrologic Forecasting Model Connected with Real Time Watershed and River Monitoring System Journal of korean society of hazard mitigation, 2013 DOI:10.9798/KOSHAM.2013.13.3.239

[18]	Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia Journal of Water Resource and Protection, 2013 DOI:10.4236/jwarp.2013.52013

[19]	Comparison of Artificial Neural Network Models for Sediment Yield Prediction at Single Gauging Station of Watershed in Eastern India Journal of Hydrologic Engineering, 2013 DOI:10.1061/(ASCE)HE.1943-5584.0000601

[20]	Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India Agricultural Water Management, 2012 DOI:10.1016/j.agwat.2011.12.005

[21]	River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia Water Resources Management, 2012 DOI:10.1007/s11269-012-9992-5

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