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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Carolinae. Geographica

ISSN

0300-5402

e-ISSN

2336-1980

Svazek periodika

58

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

187-199

Kód UT WoS článku

001127598400002

EID výsledku v databázi Scopus

2-s2.0-85182361882