Geographically weighted regression random forest for modeling soil particles

Authors

  • Atiek Iriany
    Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya University, Malang, 65144, Indonesia
  • Wigbertus Ngabu
    Mathematics Education Study Program, University of Riau, Pekanbaru, 28293, Indonesia
    https://orcid.org/0000-0002-7153-7310
  • Henny Pramoedyo
    Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya University, Malang, 65144, Indonesia
  • Amarifai
    Department of Statistics, Faculty of Mathematics and Natural Science, Brawijaya University, Malang, 65144, Indonesia

Keywords:

Clay particles, GWR, GWRRF, Hybrid model, Random forest

Abstract

Clay particles play a vital role in determining soil quality, particularly in the fields of agriculture and conservation. However, the complex and non-linear spatial distribution of clay particles is difficult to capture using conventional modeling methods. This study aims to develop a hybrid model, Geographically Weighted Regression Random Forest (GWRRF), which combines the ability of Geographically Weighted Regression (GWR) to capture spatial heterogeneity and the strength of Random Forest (RF) in handling non-linear relationships. The data used in this study were derived from soil texture and local morphologic analysis across 50 observation points in the Kalikonto watershed. The results show that the GWRRF model provides more accurate predictions of clay particle content than the GWR model, with an R² value of 0.735 and a lower RMSE. In contrast, the GWR model achieved an R² value of 0.575. The integration of both methods in GWRRF offers a novel, adaptive, and context-aware approach to understanding the distribution of clay particles, contributing to more precise and sustainable data-driven land management.

Dimensions

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Published

2026-03-14

How to Cite

Geographically weighted regression random forest for modeling soil particles. (2026). Journal of the Nigerian Society of Physical Sciences, 8(2), 2939. https://doi.org/10.46481/jnsps.2026.2939

Issue

Volume 8, Issue 2, May 2026 (In Progress)

Section

Mathematics & Statistics
Copyright & Licensing

Copyright (c) 2026 Atiek Iriany, Wigbertus Ngabu, Henny Pramoedyo, Amarifai (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Geographically weighted regression random forest for modeling soil particles. (2026). Journal of the Nigerian Society of Physical Sciences, 8(2), 2939. https://doi.org/10.46481/jnsps.2026.2939

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