Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10476888" target="_blank" >RIV/00216208:11310/23:10476888 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6W_Wvim0qd" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6W_Wvim0qd</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14712/23361980.2023.14" target="_blank" >10.14712/23361980.2023.14</a>
Alternative languages
Result language
angličtina
Original language name
Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought
Original language description
Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploit-ing 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 explan-atory 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10508 - Physical geography
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Acta Universitatis Carolinae. Geographica
ISSN
0300-5402
e-ISSN
2336-1980
Volume of the periodical
58
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
13
Pages from-to
187-199
UT code for WoS article
001127598400002
EID of the result in the Scopus database
2-s2.0-85182361882