10.1061/40569(2001)53Bridging the Gap: Meeting the World's Water and Environmental Resources Challenges - Proceedings of the World Water and Environmental Resources Congress 2001111
Time series prediction is a complex problem that consists of forecasting the future behavior of a se...
Estimation of rivers suspended load is one of the major issues of topics related to river engineerin...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
AbstractThe embedding dimension and the number of nearest neighbors are very important parameters in...
Limited literature regarding parameter estimation of dynamic systems has been identified as the cent...
In this article, two models of the forecast of time series obtained from the chaotic dynamic systems...
Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the re...
Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the re...
Interest in chaotic time series prediction has grown in recent years due to its multiple application...
In order to study forecasting of chaotic time series, artificial chaotic time series that are derive...
The gauged river data play an important role in modeling, planning and management of the river basin...
In this work, the nonlinear polynomial autoregressive (PAR) system has been applied to predict chaot...
Short-term prediction of hydrological time series using chaotic dynamical systems approach is gainin...
Five modeling strategies are employed to analyze water level time series of six lakes with different...
This paper introduces several novel strategies for multi-step-ahead prediction of chaotic time serie...
Time series prediction is a complex problem that consists of forecasting the future behavior of a se...
Estimation of rivers suspended load is one of the major issues of topics related to river engineerin...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
AbstractThe embedding dimension and the number of nearest neighbors are very important parameters in...
Limited literature regarding parameter estimation of dynamic systems has been identified as the cent...
In this article, two models of the forecast of time series obtained from the chaotic dynamic systems...
Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the re...
Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the re...
Interest in chaotic time series prediction has grown in recent years due to its multiple application...
In order to study forecasting of chaotic time series, artificial chaotic time series that are derive...
The gauged river data play an important role in modeling, planning and management of the river basin...
In this work, the nonlinear polynomial autoregressive (PAR) system has been applied to predict chaot...
Short-term prediction of hydrological time series using chaotic dynamical systems approach is gainin...
Five modeling strategies are employed to analyze water level time series of six lakes with different...
This paper introduces several novel strategies for multi-step-ahead prediction of chaotic time serie...
Time series prediction is a complex problem that consists of forecasting the future behavior of a se...
Estimation of rivers suspended load is one of the major issues of topics related to river engineerin...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
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