PREDICTION OF SOIL PARTICLES USING A SPATIALLY ADAPTIVE GEOGRAPHICALLY WEIGHTED K-NEAREST NEIGHBORS ORDINARY LOGISTIC REGRESSION APPROACH
Abstract
Soil particle prediction is crucial in various fields, including agriculture, environmental management, and geotechnical applications. The spatial variation of soil texture significantly affects land fertility, erosion risk, and construction feasibility. However, conventional statistical methods and machine learning techniques often fail to capture the complex spatial heterogeneity in soil distribution. This study proposes the Geographically Weighted K Nearest Neighbors Ordinary Logistic Regression (GWKNNOLR) method to improve the accuracy of soil particle classification by integrating geographically weighted regression with an adaptive spatial weighting mechanism using the K Nearest Neighbors (KNN) algorithm. The objective of this research is to develop and evaluate a spatially adaptive classification model that more accurately predicts soil particle categories, namely sand, silt, and clay, by incorporating local spatial dependencies using GWKNNOLR in the Kalikonto watershed (DAS Kalikonto) in Batu. This study utilizes field measurement data combined with digital terrain modeling to analyze the relationship between local morphological variables and soil texture classification (sand, silt, and clay). The study area includes 50 observation points and 8 test variables. The model's performance is compared to the Ordinary Logistic Regression (OLR) method. The results indicate that GWKNNOLR achieves a classification accuracy of 88 percent, outperforming OLR, which only reaches 80 percent. Integrating KNN as a spatial weighting mechanism enhances adaptability to variations in sample distribution, leading to more accurate predictions. These findings emphasize the importance of considering spatial dependencies in soil texture modeling. The proposed method can support sustainable land resource management, erosion risk mitigation, and precision agriculture by providing more reliable soil classification. Future research may explore further optimization of spatial weighting mechanisms and the application of this method in different geographical regions.
Downloads
References
D. Kim, B. H. Nam, and H. Youn, “EFFECT OF CLAY CONTENT ON THE SHEAR STRENGTH OF CLAY–SAND MIXTURE,” International Journal of Geo-Engineering, vol. 9, pp. 1–12, 2018.doi: https://doi.org/10.1186/s40703-018-0087-x
E. J. Parent, S.-É. Parent, and L. E. Parent, “DETERMINING SOIL PARTICLE-SIZE DISTRIBUTION FROM INFRARED SPECTRA USING MACHINE LEARNING PREDICTIONS: METHODOLOGY AND MODELING,” PLoS One, vol. 16, no. 7, p. e0233242, 2021.doi: https://doi.org/10.1371/journal.pone.0233242
J. M. Moreno-Maroto and J. Alonso-Azcarate, “EVALUATION OF THE USDA SOIL TEXTURE TRIANGLE THROUGH ATTERBERG LIMITS AND AN ALTERNATIVE CLASSIFICATION SYSTEM,” Appl Clay Sci, vol. 229, p. 106689, 2022.doi: https://doi.org/10.1016/j.clay.2022.106689
M. Heidari, S. Zalaghaie, M. R. Nikudel, and A. Seif, “PREDICTION OF THE LOESS SOILS COLLAPSE SENSITIVITY CLASSES BY APPLICATION OF ORDINAL LOGISTIC REGRESSION, NORTHEASTERN IRAN,” Iranian Journal of Engineering Geology, vol. 16, no. 2, 2023.
S. Zalaghaie, M. Heidari, M. R. Nikudel, S. H. Jalali, and A. Seif, “APPLICATION OF ORDINAL LOGISTIC AND GENE EXPRESSION PROGRAMMING METHODS TO PREDICT THE COLLAPSE SENSITIVITY CLASSES OF LOESS SOILS, A CASE STUDY: GOLESTAN PROVINCE, NORTHEASTERN IRAN,” Environ Monit Assess, vol. 195, no. 2, p. 305, 2023.doi: https://doi.org/10.1007/s10661-023-10915-5
H. Pramoedyo, W. Ngabu, S. Riza, and A. Iriany, “SPATIAL ANALYSIS USING GEOGRAPHICALLY WEIGHTED ORDINARY LOGISTIC REGRESSION (GWOLR) METHOD FOR PREDICTION OF PARTICLE-SIZE FRACTION IN SOIL SURFACE,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2024, p. 012005.doi: https://doi.org/10.1088/1755-1315/1299/1/012005
M. Kubara and K. Kopczewska, “AKAIKE INFORMATION CRITERION IN CHOOSING THE OPTIMAL K-NEAREST NEIGHBOURS OF THE SPATIAL WEIGHT MATRIX,” Spat Econ Anal, vol. 19, no. 1, pp. 73–91, 2024.doi: https://doi.org/10.1080/17421772.2023.2176539
X. Peng, R. Chen, K. Yu, F. Ye, and W. Xue, “AN IMPROVED WEIGHTED K-NEAREST NEIGHBOR ALGORITHM FOR INDOOR LOCALIZATION,” Electronics (Basel), vol. 9, no. 12, p. 2117, 2020.doi: https://doi.org/10.3390/electronics9122117
H. Pramoedyo, A. Iriany, W. Ngabu, and S. Riza, “SOIL PARTICLE PREDICTION USING SPATIAL ORDINARY LOGISTIC REGRESSION,” Advances in Decision Sciences, vol. 28, no. 1, pp. 66–92, 2024.doi: https://doi.org/10.47654/v28y2024i2p66-92
X. Yang, Y. Yang, S. Xu, J. Han, Z. Chai, and G. Yang, “A NEW ALGORITHM FOR LARGE-SCALE GEOGRAPHICALLY WEIGHTED REGRESSION WITH K-NEAREST NEIGHBORS,” ISPRS Int J Geoinf, vol. 12, no. 7, p. 295, 2023.doi: https://doi.org/10.3390/ijgi12070295
P. Jafary, D. Shojaei, A. Rajabifard, and T. Ngo, “AUTOMATING PROPERTY VALUATION AT THE MACRO SCALE OF SUBURBAN LEVEL: A MULTI-STEP METHOD BASED ON SPATIAL IMPUTATION TECHNIQUES, MACHINE LEARNING AND DEEP LEARNING,” Habitat Int, vol. 148, p. 103075, 2024.doi: https://doi.org/10.1016/j.habitatint.2024.103075
W. Ngabu, R. Fitriani, H. Pramoedyo, and A. B. Astuti, “CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 2, pp. 945–954, 2023.doi: https://doi.org/10.30598/barekengvol17iss2pp0945-0954
F. M. Seaton, P. B. L. George, I. Lebron, D. L. Jones, S. Creer, and D. A. Robinson, “SOIL TEXTURAL HETEROGENEITY IMPACTS BACTERIAL BUT NOT FUNGAL DIVERSITY,” Soil Biol Biochem, vol. 144, p. 107766, 2020.doi: https://doi.org/10.1016/j.soilbio.2020.107766
L. Xiong, G. Tang, X. Yang, and F. Li, “GEOMORPHOLOGY-ORIENTED DIGITAL TERRAIN ANALYSIS: PROGRESS AND PERSPECTIVES,” Journal of geographical sciences, vol. 31, pp. 456–476, 2021.doi: https://doi.org/10.1007/s11442-021-1853-9
A. Iriany, W. Ngabu, D. Ariyanto, and H. Pramoedyo, “KRIGING PREDICTION AND SIMULATION MODEL: ANALYSIS OF SURFACE SOIL PARTICLE SIZE DISTRIBUTION.,” Mathematical Modelling of Engineering Problems, vol. 12, no. 4, 2025, doi: https://doi.org/10.18280/mmep.120408 .
Z. Guo et al., “SOIL TEXTURE IS AN IMPORTANT FACTOR DETERMINING HOW MICROPLASTICS AFFECT SOIL HYDRAULIC CHARACTERISTICS,” Environ Int, vol. 165, p. 107293, 2022.doi: https://doi.org/10.1016/j.envint.2022.107293
H. Pramoedyo, W. Ngabu, S. Riza, and L. Amaliana, “SINGLE BOOTSTRAP APPROACH WITH GEOGRAPHICALLY WEIGHT REGRESSION MODELING USING PARTICLE-SIZE FRACTION,” J Theor Appl Inf Technol, vol. 31, no. 10, 2023, [Online]. Available: www.jatit.org
R. J. Tallarida, R. B. Murray, R. J. Tallarida, and R. B. Murray, “CHI-SQUARE TEST,” Manual of pharmacologic calculations: with computer programs, pp. 140–142, 1987.doi: https://doi.org/10.1007/978-1-4612-4974-0_43
M. Alrumaidhi and H. A. Rakha, “FACTORS AFFECTING CRASH SEVERITY AMONG ELDERLY DRIVERS: A MULTILEVEL ORDINAL LOGISTIC REGRESSION APPROACH,” Sustainability, vol. 14, no. 18, p. 11543, 2022.doi: https://doi.org/10.3390/su141811543
H. Khaulasari and F. Suhaeri, “MAPPING OF GENDER INEQUALITY IN INDONESIA BASED ON INFLUENCING FACTORS USING GEOGRAPHICALLY WEIGHTED ORDINAL LOGISTIC REGRESSION,” BAREKENG: Jurnal Ilmu Matematika Dan Terapan, vol. 18, no. 1, pp. 233–244, 2024.doi: https://doi.org/10.30598/barekengvol18iss1pp0233-0244
J. Liang, G. Bi, and C. Zhan, “MULTINOMIAL AND ORDINAL LOGISTIC REGRESSION ANALYSES WITH MULTI-CATEGORICAL VARIABLES USING R,” Ann Transl Med, vol. 8, no. 16, p. 982, 2020.doi: https://doi.org/10.21037/atm-2020-57
B. French and M. S. Shotwell, “REGRESSION MODELS FOR ORDINAL OUTCOMES,” JAMA, vol. 328, no. 8, pp. 772–773, 2022.doi: https://doi.org/10.1001/jama.2022.12104
B. S. Tusa, S. A. Kebede, and A. B. Weldesenbet, “SPATIAL DISTRIBUTION AND DETERMINANT FACTORS OF ANEMIA AMONG ADULTS AGED 15–59 IN ETHIOPIA; USING MIXED-EFFECTS ORDINAL LOGISTIC REGRESSION MODEL,” BMC Nutr, vol. 7, pp. 1–12, 2021.doi: https://doi.org/10.1186/s40795-021-00424-4
I. O. Asare and A. C. Mensah, “CRASH SEVERITY MODELLING USING ORDINAL LOGISTIC REGRESSION APPROACH,” Int J Inj Contr Saf Promot, vol. 27, no. 4, pp. 412–419, 2020.doi: https://doi.org/10.1080/17457300.2020.1790615
W. Ngabu, H. Pramoedyo, A. Iriany, and R. Fitriani, “HYBRID MODELING OF DEEP NEURAL NETWORKS AND ORDINARY LOGISTIC REGRESSION FOR PREDICTING GROUNDWATER QUALITY,” Mathematical Modelling of Engineering Problems, vol. 12, no. 3, pp. 774–786, 2025, doi: https://doi.org/10.18280/mmep.120304.
G. Tutz, “ORDINAL REGRESSION: A REVIEW AND A TAXONOMY OF MODELS,” Wiley Interdiscip Rev Comput Stat, vol. 14, no. 2, p. e1545, 2022.doi: https://doi.org/10.1002/wics.1545
S. Jayawardena, J. Epps, and E. Ambikairajah, “ORDINAL LOGISTIC REGRESSION WITH PARTIAL PROPORTIONAL ODDS FOR DEPRESSION PREDICTION,” IEEE Trans Affect Comput, vol. 14, no. 1, pp. 563–577, 2020.doi: https://doi.org/10.1109/TAFFC.2020.3031300
A. Iriany, W. Ngabu, and D. Ariyanto, “RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 18, no. 1, pp. 627–636, 2024.doi: https://doi.org/10.30598/barekengvol18iss1pp0627-0636
A. Comber et al., “THE GWR ROUTE MAP: A GUIDE TO THE INFORMED APPLICATION OF GEOGRAPHICALLY WEIGHTED REGRESSION,” arXiv preprint arXiv:2004.06070, 2020.
V. N. Mishra, V. Kumar, R. Prasad, and M. Punia, “GEOGRAPHICALLY WEIGHTED METHOD INTEGRATED WITH LOGISTIC REGRESSION FOR ANALYZING SPATIALLY VARYING ACCURACY MEASURES OF REMOTE SENSING IMAGE CLASSIFICATION,” Journal of the Indian Society of Remote Sensing, vol. 49, pp. 1189–1199, 2021.doi: https://doi.org/10.1007/s12524-020-01286-2
S. Zhang, S. Ray, R. Lu, and Y. Zheng, “SPRIG: A LEARNED SPATIAL INDEX FOR RANGE AND KNN QUERIES,” in Proceedings of the 17th International Symposium on Spatial and Temporal Databases, 2021, pp. 96–105.doi: https://doi.org/10.1145/3469830.3470892
S. Liu, R. De Lacerda, and J. Fiorina, “PERFORMANCE ANALYSIS OF ADAPTIVE K FOR WEIGHTED K-NEAREST NEIGHBOR BASED INDOOR POSITIONING,” in 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring), IEEE, 2022, pp. 1–5.doi: https://doi.org/10.1109/VTC2022-Spring54318.2022.9860699
X. Chen and Y. Chen, “QUANTIFYING THE RELATIONSHIPS BETWEEN NETWORK DISTANCE AND STRAIGHT-LINE DISTANCE: APPLICATIONS IN SPATIAL BIAS CORRECTION,” Ann GIS, vol. 27, no. 4, pp. 351–369, 2021.doi: https://doi.org/10.1080/19475683.2021.1966503
X. Yang, Y. Yang, S. Xu, J. Han, Z. Chai, and G. Yang, “A NEW ALGORITHM FOR LARGE-SCALE GEOGRAPHICALLY WEIGHTED REGRESSION WITH K-NEAREST NEIGHBORS,” ISPRS Int J Geoinf, vol. 12, no. 7, p. 295, 2023.doi: https://doi.org/10.3390/ijgi12070295
M. K. Campbell, A. Donner, and K. M. Webster, “ARE ORDINAL MODELS USEFUL FOR CLASSIFICATION?,” Stat Med, vol. 10, no. 3, pp. 383–394, 1991.doi: https://doi.org/10.1002/sim.4780100310
Copyright (c) 2025 Henny Pramoedyo, Wigbertus Ngabu, Atiek Iriany, Sativandi Riza
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
