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Prediction of Wind Speeds based on Digital Elevation Models using Boosted Regression Trees

Fischer, Peter and Etienne, Christophe and Tian, Jiaojiao and Krauß, Thomas (2015) Prediction of Wind Speeds based on Digital Elevation Models using Boosted Regression Trees. 3rd International Conference on Sensors and Models in Photogrammetry and Remote Sensing, 23.-25. Nov. 2015, Kish Island, Iran.

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Abstract

In this paper a new approach is presented to predict maximum wind speeds using Gradient Boosted Regression Trees (GBRT). GBRT are a non-parametric regression technique used in various applications, suitable to make predictions without having an in-depth a-priori knowledge about the functional dependancies between the predictors and the response variables. Our aim is to predict maximum wind speeds based on predictors, which are derived from a digital elevation model (DEM). The predictors describe the orography of the Area-of-Interest (AoI) by various means like first and second order derivatives of the DEM, but also higher sophisticated classifications describing exposure and shelterness of the terrain to wind flux. In order to take the different scales into account which probably influence the streams and turbulences of wind flow over complex terrain, the predictors are computed on different spatial resolutions ranging from 30 m up to 2000 m. The geographic area used for examination of the approach is Switzerland, a mountainious region in the heart of europe, dominated by the alps, but also covering large valleys. The full workflow is described in this paper, which consists of data preparation using image processing techniques, model training using a state-of-the-art machine learning algorithm, in-depth analysis of the trained model, validation of the model and application of the model to generate a wind speed map.

Item URL in elib:https://elib.dlr.de/99861/
Document Type:Conference or Workshop Item (Speech)
Title:Prediction of Wind Speeds based on Digital Elevation Models using Boosted Regression Trees
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Fischer, PeterPeter.Fischer (at) dlr.deUNSPECIFIED
Etienne, ChristopheChristophe.Etienne (at) secquaero.comUNSPECIFIED
Tian, Jiaojiaojiaojiao.tian (at) dlr.deUNSPECIFIED
Krauß, Thomasthomas.krauss (at) dlr.deUNSPECIFIED
Date:November 2015
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-6
Status:Published
Keywords:Spatial Predictions, Non-Parametric Regression, Regression Trees, Wind Speeds
Event Title:3rd International Conference on Sensors and Models in Photogrammetry and Remote Sensing
Event Location:Kish Island, Iran
Event Type:international Conference
Event Dates:23.-25. Nov. 2015
Organizer:University of Tehran
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Fischer, Peter
Deposited On:25 Nov 2015 15:31
Last Modified:31 Jul 2019 19:56

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