Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia

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Journal of Water Resource and Protection

ISSN Print: 1945-3094
ISSN Online: 1945-3108
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"Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia"
written by Sokchhay Heng, Tadashi Suetsugi,
published by Journal of Water Resource and Protection, Vol.5 No.2, 2013

has been cited by the following article(s):

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[1]	Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model Water Resources Research, 2024 DOI:10.1029/2023WR034557

[2]	Quantitative forecasting of bed sediment load in river engineering: an investigation into machine learning methodologies for complex phenomena Water Supply, 2024 DOI:10.2166/ws.2024.017

[3]	Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model Water Resources Research, 2024 DOI:10.1029/2023WR034557

[4]	Deep Neural Network to Classify Seabed Sediment Using MBES Multifrequency IOP Conference Series: Earth and Environmental Science, 2023 DOI:10.1088/1755-1315/1276/1/012058

[5]	Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches Water, 2022 DOI:10.3390/w14193098

[6]	Sediment-yield prediction using map correlation method Materials Today: Proceedings, 2022 DOI:10.1016/j.matpr.2022.03.504

[7]	Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm Water, 2022 DOI:10.3390/w14182815

[8]	Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions Acta Geophysica, 2022 DOI:10.1007/s11600-022-00894-5

[9]	Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches Water, 2022 DOI:10.3390/w14193098

[10]	Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions Acta Geophysica, 2022 DOI:10.1007/s11600-022-00894-5

[11]	A regional ANN-based model to estimate suspended sediment concentrations in ungauged heterogeneous basins Hydrological Sciences Journal, 2021 DOI:10.1080/02626667.2021.1918695

[12]	Sediment deposition and distribution modelling in reservoirs: current trends and prospects Proceedings of the Institution of Civil Engineers - Water Management, 2020 DOI:10.1680/jwama.19.00055

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[14]	Prediction of bed load sediments using different artificial neural network models Frontiers of Structural and Civil Engineering, 2020 DOI:10.1007/s11709-019-0600-0

[15]	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

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

[17]	Computational Intelligence in Sensor Networks Studies in Computational Intelligence, 2019 DOI:10.1007/978-3-662-57277-1_20

[18]	Wavelet-Exponential Smoothing: a New Hybrid Method for Suspended Sediment Load Modeling Environmental Processes, 2019 DOI:10.1007/s40710-019-00363-0

[19]	The Use of Sediment Rating Curve under its Limitations to Estimate the Suspended Load Reviews in Agricultural Science, 2019 DOI:10.7831/ras.7.0_88

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[21]	A regional suspended load yield estimation model for ungauged watersheds Water Science and Engineering, 2018 DOI:10.1016/j.wse.2018.09.008

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[23]	The application of backpropagation neural network method to estimate the sediment loads MATEC Web of Conferences, 2017 DOI:10.1051/matecconf/201710105016

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[27]	Development of sediment load estimation models by using artificial neural networking techniques Environmental Monitoring and Assessment, 2015 DOI:10.1007/s10661-015-4866-y

[28]	Integrative neural networks model for prediction of sediment rating curve parameters for ungauged basins Journal of Hydrology, 2015 DOI:10.1016/j.jhydrol.2015.11.008

[29]	Regionalization of sediment rating curve for sediment yield prediction in ungauged catchments Hydrology Research, 2015 DOI:10.2166/nh.2013.090

[30]	Comparison of regionalization approaches in parameterizing sediment rating curve in ungauged catchments for subsequent instantaneous sediment yield prediction Journal of Hydrology, 2014 DOI:10.1016/j.jhydrol.2014.03.003

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

[32]	Development of a regional model for catchment-scale suspended sediment yield estimation in ungauged rivers of the Lower Mekong Basin Geoderma, 2014 DOI:10.1016/j.geoderma.2014.07.030

[33]	Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction Journal of Water Resource and Protection, 2013 DOI:10.4236/jwarp.2013.54039

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