Stochasticity Nonlinearity and Forecasting of Streamflow Processes

This book PDF is perfect for those who love Computers genre, written by Wen Wang and published by IOS Press which was released on 28 March 2024 with total hardcover pages 220. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Stochasticity Nonlinearity and Forecasting of Streamflow Processes books below.

Stochasticity  Nonlinearity and Forecasting of Streamflow Processes
Author : Wen Wang
File Size : 53,9 Mb
Publisher : IOS Press
Language : English
Release Date : 28 March 2024
ISBN : 1586036211
Pages : 220 pages
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Stochasticity Nonlinearity and Forecasting of Streamflow Processes by Wen Wang Book PDF Summary

Streamflow forecasting is of great importance to water resources management and flood defense. On the other hand, a better understanding of the streamflow process is fundamental for improving the skill of streamflow forecasting. The methods for forecasting streamflows may fall into two general classes: process-driven methods and data-driven methods. Equivalently, methods for understanding streamflow processes may also be broken into two categories: physically-based methods and mathematically-based methods. This thesis focuses on using mathematically-based methods to analyze stochasticity and nonlinearity of streamflow processes based on univariate historic streamflow records, and presents data-driven models that are also mainly based on univariate streamflow time series. Six streamflow processes of five rivers in different geological regions are investigated for stochasticity and nonlinearity at several characteristic timescales.

Stochasticity  Nonlinearity and Forecasting of Streamflow Processes

Streamflow forecasting is of great importance to water resources management and flood defense. On the other hand, a better understanding of the streamflow process is fundamental for improving the skill of streamflow forecasting. The methods for forecasting streamflows may fall into two general classes: process-driven methods and data-driven methods. Equivalently,

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