Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale

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Journal of Water Resource and Protection

ISSN Print: 1945-3094
ISSN Online: 1945-3108
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"Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale"
written by Parag P. Bhagwat, Rajib Maity,
published by Journal of Water Resource and Protection, Vol.4 No.7, 2012

has been cited by the following article(s):

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[1]	Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023 DOI:10.1007/s40996-022-00947-1

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[3]	Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM H2Open Journal, 2022 DOI:10.2166/h2oj.2022.134

[4]	A deep multivariate time series multistep forecasting network Applied Intelligence, 2022 DOI:10.1007/s10489-021-02899-x

[5]	Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2022 DOI:10.1007/s40996-022-00947-1

[6]	Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models Environmental Processes, 2022 DOI:10.1007/s40710-022-00602-x

[7]	Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting Water Resources Management, 2021 DOI:10.1007/s11269-020-02746-7

[8]	Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting Water Resources Management, 2021 DOI:10.1007/s11269-020-02746-7

[9]	Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China Natural Hazards, 2021 DOI:10.1007/s11069-020-04394-x

[10]	Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan Meteorological Applications, 2020 DOI:10.1002/met.1881

[11]	Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan Meteorological Applications, 2020 DOI:10.1002/met.1881

[12]	Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting Applied Sciences, 2020 DOI:10.3390/app10093224

[13]	Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling Water, 2019 DOI:10.3390/w11030451

[14]	Energy consumption forecasting in agriculture by artificial intelligence and mathematical models Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019 DOI:10.1080/15567036.2019.1604872

[15]	Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China Water, 2019 DOI:10.3390/w11010085

[16]	Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning Transportmetrica A: Transport Science, 2019 DOI:10.1080/23249935.2019.1637966

[17]	Climate Change Impacts Water Science and Technology Library, 2018 DOI:10.1007/978-981-10-5714-4_6

[18]	Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models Neural Computing and Applications, 2018 DOI:10.1007/s00521-018-3519-9

[19]	Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin Science of The Total Environment, 2018 DOI:10.1016/j.scitotenv.2017.12.025

[20]	Space–time forecasting of groundwater level using a hybrid soft computing model Hydrological Sciences Journal, 2017 DOI:10.1080/02626667.2016.1252986

[21]	Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction International Journal of Environmental Science and Technology, 2016 DOI:10.1007/s13762-015-0856-4

[22]	Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques Water Resources Management, 2016 DOI:10.1007/s11269-015-1188-3

[23]	Uncovering global climate fields causing local precipitation extremes Hydrological Sciences Journal, 2016 DOI:10.1080/02626667.2015.1006232

[24]	A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction Environmental Earth Sciences, 2016 DOI:10.1007/s12665-015-5096-x

[25]	Modeling river discharge time series using support vector machine and artificial neural networks Environmental Earth Sciences, 2016 DOI:10.1007/s12665-016-5435-6

[26]	Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate Journal of Hydrology, 2016 DOI:10.1016/j.jhydrol.2016.04.041

[27]	Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach Energy and Buildings, 2015 DOI:10.1016/j.enbuild.2014.12.029

[28]	Multi-step-ahead time series prediction using multiple-output support vector regression Neurocomputing, 2014 DOI:10.1016/j.neucom.2013.09.010

[29]	Support vector machine applications in the field of hydrology: A review Applied Soft Computing, 2014 DOI:10.1016/j.asoc.2014.02.002

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