Investigating the performance of genetic algorithms selection method in estimating stand-level structural and biophysical variables of lowland dipterocarp forest from LiDAR data

Genetic Algorithms (GAs) methods are rarely been used as a selection method in developing the model for estimating forest's variables as compared to stepwise regression method. This study aims to investigate the performance of GAs as a selection method in finding the best predictor variables. F...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2018-06, Vol.169 (1), p.12110
Main Authors: Misman, M A, Yaakub, S Y, Omar, H
Format: Article
Language:English
Subjects:
Algorithms
Estimation
Forests
Genetic algorithms
Lidar
Methods
Production methods
Regression
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Summary:Genetic Algorithms (GAs) methods are rarely been used as a selection method in developing the model for estimating forest's variables as compared to stepwise regression method. This study aims to investigate the performance of GAs as a selection method in finding the best predictor variables. Five models were developed to test the performance of GAs methods in estimating stand-level structural and biophysical variables of the forest, namely as mean height (Hm), stand density (S), basal area (G) square mean diameter (Dg), and biomass (B). The results have shown GAs methods produce better model for Hm and Dg as compared to stepwise regression method in term of adjusted R2 and RMSE values. However, models for S, G and B based on stepwise regression method outperformed the GAs methods. This study has shown the capability of GAs in finding the best airborne lidar scanning (ALS) metrics for the development of best model in estimating stand-level structural and biophysical variables of lowland dipterocarp forest.
ISSN:1755-1307
1755-1315
1755-1315
DOI:10.1088/1755-1315/169/1/012110