AUC GEOGRAPHICA

AUC GEOGRAPHICA

We are pleased to share that AUC Geographica was awarded an Impact Factor of 0.5 in the 2023 Journal Citation Reports™ released by Clarivate in June 2024. AUC Geographica ranks in Q3 in the field of Geography.

AUC Geographica (Acta Universitatis Carolinae Geographica) is a scholarly academic journal continuously published since 1966 that publishes research in the broadly defined field of geography: physical geography, geo-ecology, regional, social, political and economic geography, regional development, cartography, geoinformatics, demography and geo-demography.

AUC Geographica also publishes articles that contribute to advances in geographic theory and methodology and address the questions of regional, socio-economic and population policy-making in Czechia.

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Release dates: June 30, December 31

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The journal has been indexed by the Polish Ministry of Science and Higher Education (MSHE) on the list of scientific journals recommended for authors to publish their articles. ICI World of Journals; Acta Universitatis Carolinae, Geographica.

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Impact factor (JCR®): 0.6
Journal Citation Indicator (JCI): 0.24
Rank (JCI): Q3 in Geography

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Cite Score: 1.1
Rank (ASJC): Q3 in Geography, Planning and Development; Q3 in General Earth and Planetary Sciences

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AUC GEOGRAPHICA, 187–199

Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought

Daniel Bicák

DOI: https://doi.org/10.14712/23361980.2023.14
published online: 01. 11. 2023

abstract

Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploiting the underlying spatial relationships present in the geographical data. One of the approaches, which addresses this problem, is based on an ensemble of local models, which are constructed from samples in close proximity to the location of prediction. This concept was applied to the Random Forest (RF) algorithm, creating a Geographical Random Forest (GRF). This study aims to further develop GRF by tuning the spatial parameters for each location in case of agricultural drought. In addition to tuning, the explanatory property of RF within the framework GRF is explored. Four machine learning models were constructed; regular RF, regular RF with spatial covariates, GRF, and GRF with the tuning of spatial parameters. Models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Although the decrease in RMSE in this very case is relatively small, the method may provide higher improvement with different datasets.

keywords: machine learning; Random Forest; Geographical Random Forest

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Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought is licensed under a Creative Commons Attribution 4.0 International License.

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ISSN: 0300-5402
E-ISSN: 2336-1980

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