Application of Support Vector Regression for Modeling Low Flow Time Series
暂无分享,去创建一个
[1] Ozgur Kisi,et al. New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine , 2018 .
[2] Abbas Parsaie,et al. Applications of soft computing techniques for prediction of energy dissipation on stepped spillways , 2016, Neural Computing and Applications.
[3] Abbas Parsaie,et al. Investigation of trap efficiency of retention dams , 2018 .
[4] Mohammad Najafzadeh,et al. GMDH-GEP to predict free span expansion rates below pipelines under waves , 2018 .
[5] A. Parsaie,et al. Prediction of Energy Dissipation of Flow Over Stepped Spillways Using Data-Driven Models , 2018 .
[6] Mohammad Najafzadeh,et al. Prediction of riprap stone size under overtopping flow using data-driven models , 2018 .
[7] Abbas Parsaie,et al. Water quality prediction using machine learning methods , 2018 .
[8] Mohammad Najafzadeh,et al. Optimized expressions to evaluate the flow discharge in main channels and floodplains using evolutionary computing and model classification , 2018 .
[9] Abbas Parsaie,et al. Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques , 2017 .
[10] Abbas Parsaie,et al. Hydrochemical evaluation of river water quality—a case study , 2017, Applied Water Science.
[11] Mohammad Najafzadeh,et al. NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects , 2017 .
[12] Pijush Samui,et al. Forecasting Evaporative Loss by Least-Square Support-Vector Regression and Evaluation with Genetic Programming, Gaussian Process, and Minimax Probability Machine Regression: Case Study of Brisbane City , 2017 .
[13] Abbas Parsaie,et al. Computational Modeling of Pollution Transmission in Rivers , 2017, Applied Water Science.
[14] A. Parsaie,et al. Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method , 2017 .
[15] A. Parsaie,et al. Mathematical expression of discharge capacity of compound open channels using MARS technique , 2017, Journal of Earth System Science.
[16] Amir Hamzeh Haghiabi,et al. Modeling River Mixing Mechanism Using Data Driven Model , 2017, Water Resources Management.
[17] A. Parsaie,et al. Physical and numerical modeling of performance of detention dams , 2017 .
[18] Abbas Parsaie,et al. Prediction of head loss on cascade weir using ANN and SVM , 2017 .
[19] Kuk-Hyun Ahn,et al. Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin , 2016 .
[20] Abbas Parsaie,et al. Prediction of side weir discharge coefficient by support vector machine technique , 2016 .
[21] R. Deo,et al. Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models , 2016, Stochastic Environmental Research and Risk Assessment.
[22] A. Haghiabi,et al. Prediction of longitudinal dispersion coefficient using multivariate adaptive regression splines , 2016, Journal of Earth System Science.
[23] Jaya Kandasamy,et al. Prediction of hydrological time-series using extreme learning machine , 2016 .
[24] R. Deo,et al. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland , 2016, Environmental Monitoring and Assessment.
[25] Ravinesh C. Deo,et al. Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia , 2015 .
[26] Umut Okkan,et al. Bayesian Learning and Relevance Vector Machines Approach for Downscaling of Monthly Precipitation , 2015 .
[27] Yu Lei,et al. Prediction of length-of-day using extreme learning machine , 2015 .
[28] Chandranath Chatterjee,et al. Regional Flood Frequency Analysis using Soft Computing Techniques , 2015, Water Resources Management.
[29] Sungwon Kim,et al. Multistep-ahead flood forecasting using wavelet and data-driven methods , 2015, KSCE Journal of Civil Engineering.
[30] B. K. Panigrahi,et al. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine , 2014, Climate Dynamics.
[31] Paresh Chandra Deka,et al. Support vector machine applications in the field of hydrology: A review , 2014, Appl. Soft Comput..
[32] H. Azamathulla,et al. Group method of data handling to predict scour depth around bridge piers , 2013, Neural Computing and Applications.
[33] Jan Adamowski,et al. Urban water demand forecasting and uncertainty assessment using ensemble wavelet‐bootstrap‐neural network models , 2013 .
[34] Arjen Ysbert Hoekstra,et al. Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times , 2013 .
[35] Jean-Philippe Vidal,et al. Low Flows in France and their relationship to large scale climate indices , 2013 .
[36] J. Abbot,et al. Application of artificial neural networks to rainfall forecasting in Queensland, Australia , 2012, Advances in Atmospheric Sciences.
[37] Amir Hossein Alavi,et al. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .
[38] O. Kisi,et al. Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran) , 2011 .
[39] Kwok-Wing Chau,et al. Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..
[40] Jagadeesh Anmala,et al. Rainfall-Runoff Modeling Using Artificial Neural Networks , 2010 .
[41] Chuntian Cheng,et al. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .
[42] Wang Tao,et al. Application of Artificial Neural Networks to Forecasting Ice Conditions of the Yellow River in the Inner Mongolia Reach , 2008 .
[43] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[44] Vijay P. Singh,et al. Critical appraisal of methods for the assessment of environmental flows and their application in two river systems of India , 2008 .
[45] C. Sivapragasam,et al. Genetic programming approach for flood routing in natural channels , 2008 .
[46] Dawei Han,et al. Flood forecasting using support vector machines , 2007 .
[47] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[48] I-Fan Chang,et al. Support vector regression for real-time flood stage forecasting , 2006 .
[49] Chuntian Cheng,et al. Using support vector machines for long-term discharge prediction , 2006 .
[50] MohammadSajjad Khan,et al. Application of Support Vector Machine in Lake Water Level Prediction , 2006 .
[51] Nitin Muttil,et al. Discharge Rating Curve Extension – A New Approach , 2005 .
[52] Chuntian Cheng,et al. Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models , 2005, ICNC.
[53] Stefano Alvisi,et al. Water level forecasting through fuzzy logic and artificial neural network approaches , 2005 .
[54] G. Blöschl,et al. Low flow estimates from short stream flow records—a comparison of methods , 2005 .
[55] K. P. Sudheer,et al. Explaining the internal behaviour of artificial neural network river flow models , 2004 .
[56] Juan B. Valdés,et al. NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .
[57] A. Soldati,et al. Artificial neural network approach to flood forecasting in the River Arno , 2003 .
[58] A. Ramachandra Rao,et al. Linearity analysis on stationary segments of hydrologic time series , 2003 .
[59] K. P. Sudheer,et al. A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .
[60] T. McMahon,et al. Stochastic generation of annual, monthly and daily climate data: A review , 2001 .
[61] E. Toth,et al. Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .
[62] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[63] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[64] Gunnar Rätsch,et al. Predicting Time Series with Support Vector Machines , 1997, ICANN.
[65] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[66] Peter Freeman,et al. Application of artificial intelligence , 1988, SOEN.
[67] J. Dracup,et al. On the definition of droughts , 1980 .
[68] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[69] Mohammad Najafzadeh,et al. Scour prediction in long contractions using ANFIS and SVM , 2016 .
[70] Zahraie Banafsheh,et al. SEASONAL METEOROLOGICAL DROUGHT PREDICTION USING SUPPORT VECTOR MACHINE , 2012 .
[71] Anteneh Meshesha Belayneh,et al. Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression , 2012, Appl. Comput. Intell. Soft Comput..
[72] Vladimir U. Smakhtin,et al. A review of methods of hydrological estimation at ungauged sites in India , 2008 .
[73] D. Basak,et al. Support Vector Regression , 2008 .
[74] Radko Mesiar,et al. Comparison of forecasting performance of nonlinear models of hydrological time series , 2006 .
[75] Marcella Cannarozzo,et al. Multi-year drought frequency analysis at multiple sites by operational hydrology - A comparison of methods , 2006 .
[76] P. C. Nayak,et al. A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .
[77] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[78] David J. C. MacKay,et al. Bayesian Methods for Backpropagation Networks , 1996 .
[79] K. Hipel,et al. Time series modelling of water resources and environmental systems , 1994 .
[80] G. Czapar,et al. [Water quality]. , 1992, Verhandelingen - Koninklijke Academie voor Geneeskunde van Belgie.