Professor Sidney J. Yakowitz

Yakowitz, D. S.

doi:10.1007/0-306-48102-2_1

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K-nn Bootstrap Models1

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2010

2022

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“…62,63 Theoretical basis for using the K-NN density estimators for time series forecasting has been well developed. [64][65][66][67][68] This approach has been used for simulating rainfall and other weather variables in the context of stochastic weather generators. 69 Details on the application of this for time series modeling and simulation are discussed later in this chapter.…”

Section: K-nn Bootstrap Modelsmentioning

confidence: 99%

Stochastic Methods for Modeling Precipitation and Streamflow

Rajagopalan¹,

Salas²

2010

Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting

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Stochastic simulation and forecasting of hydroclimatic processes, such as precipitation and streamflow, are vital tools for risk-based management of water resources systems. Stochastic hydrology has a long and rich history in this area. The traditional approaches have been based on mathematical models with assumed or derived structure representing the underlying mechanisms and processes involved. The model generally includes several variables and a parameter set. Such "parametric models" have been quite useful in practice for analyzing and synthesizing hydrologic time series at various timescales. A lot of experience has been gained using such "traditional techniques" with

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