GI_Forum 2022, Volume 10, Issue 1
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2022, Volume 10, Issue 1 ISSN 2308-1708 Online Edition ISBN 978-3-7001-9287-9 Online Edition
Hannah Kemper
S. 58 - 76 doi:10.1553/giscience2022_01_s58 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2022_01_s58
Abstract: Drought is among the most common but least understood phenomena that affect an increasing number of people in the context of climate change. To understand underlying drought dynamics affecting the local agricultural production in Botswana, a broad database comprising climatic and remote-sensing data together with socioeconomic indicators was set up. A data science approach that includes statistical and machine learning methods was chosen to retrieve information applicable in a drought early-warning system. The aim of the study was to examine how data science can contribute to the understanding of drought risk through the integration of various data sources. Different regression models (including linear and OLS) were applied. Naïve Bayes classification and Random Forest regression were included, as was a change point analysis. The impacts of two variables in particular, the Standardized Precipitation Index (SPI) and the Southern Oscillation Index (SOI), on crop productivity could be observed, highlighting possible national and regional thresholds. Further development of the early warning system, including validation, should be accompanied by ground-truth information and work with local partners. Keywords: data science, machine learning, agricultural production, drought early-warning, remote sensing Published Online: 2022/06/29 12:00:03 Object Identifier: 0xc1aa5576 0x003d880d Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |