The application of the concept of chaos theory in hydrology has been gaining considerable
interest in recent times. However, studies reporting the existence of chaos in hydrological …

Real time operation studies such as reservoir operation, flood forecasting, etc., necessitates
good forecasts of the associated hydrologic variable (s). A significant improvement in such …

Data-driven techniques such as Auto-Regressive Moving Average (ARMA), K-Nearest-
Neighbors (KNN), and Artificial Neural Networks (ANN), are widely applied to hydrologic …

The past two decades of research on chaos theory in geophysics has brought about a
significant shift in the way we view geophysical phenomena. Research on chaos theory in …

In this paper, missing consecutive streamflows are estimated, using the principles of chaos
theory, in two steps. First, the existence of chaotic behavior in the daily flows of the river is …

This study demonstrates a combined application of chaos theory and support vector
machine (SVM) in the analysis of chaotic time series with a very large sample data record. A …

The significance of treating rainfall as a chaotic system instead of a stochastic system for a
better understanding of the underlying dynamics has been taken up by various studies …

The importance of high‐resolution rainfall data to understanding the intricacies of the
dynamics of hydrological processes and describing them in a sophisticated and accurate …

The goal of the paper is to present a model for generating daily precipitation time series and
its applications to two climatologically different areas. The rainfall is modeled as stochastic …

The issues of noise reduction and the reliability of its application to hydrologic time series
are discussed. First, the concepts of noise, its effect, and noise reduction are briefly …