Improved random forest algorithm.

<div><p>Ascertaining the precise and accurate spatial distribution of population is essential in conducting effective urban planning, resource allocation, and emergency rescue planning. The random forest (RF) model is widely used in population spatialization studies. However, the complex...

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Bibliographic Details
Main Author:	Zhen Zhao (159931) (author)
Other Authors:	Hongmei Guo (754768) (author), Xueli Jiang (13504213) (author), Ying Zhang (40767) (author), Changjiang Lu (13833735) (author), Can Zhang (56016) (author), Zonghang He (20996183) (author)
Published:	2025
Subjects:	Biotechnology Ecology Plant Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified emergency rescue planning accurate spatial distribution 7 %, indicating optimal feature subsets population spatialization studies population spatial distribution population distribution characteristics integrates feature selection mean decrease accuracy optimal feature subset varying degrees compared improved rf built improved random forest div >< p 2 </ sup population spatialization model feature factors corresponding results predicted using enhance model accuracy bootstrap sampling method population dataset generated feature selection random forest bootstrap sampling worldpop dataset results showed training subset subset increased proposed method method proposed rf model prediction accuracy model construction highest accuracy resource allocation proposed herein namely mic mic ), lowest mape highest r 913 among 500m resolution
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Improved random forest algorithm.

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