Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees
نویسندگان
چکیده
منابع مشابه
ERA-Interim/Land: a global land surface reanalysis data set
ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for MediumRange Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanaly...
متن کاملOutlier Detection by Boosting Regression Trees
A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...
متن کاملCorrecting InSAR Topographically Correlated Tropospheric Delays Using a Power Law Model Based on ERA-Interim Reanalysis
Tropospheric delay caused by spatiotemporal variations in pressure, temperature, and humidity in the lower troposphere remains one of the major challenges in Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring applications. Acquiring an acceptable level of accuracy (millimeter-level) for small amplitude surface displacement is difficult without proper delay estimation. Tropo...
متن کاملGradient Boosting With Piece-Wise Linear Regression Trees
Gradient boosting using decision trees as base learners, so called Gradient Boosted Decision Trees (GBDT), is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, various GDBT construction algorithms and implementation have been designed and heavily optimized in some very popular open sourced toolkits such as XGBoost and LightGBM. In this paper, ...
متن کاملForecasting Daily Volatility Using Range-based Data
Users of agricultural markets frequently need to establish accurate representations of expected future volatility. The fact that range-based volatility estimators are highly efficient has been acknowledged in the literature. However, it is not clear whether using range-based data leads to better risk management decisions. This paper compares the performance of GARCH models, range-based GARCH mo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Hydrology and Earth System Sciences
سال: 2020
ISSN: 1607-7938
DOI: 10.5194/hess-24-2343-2020