KAJIAN SISTEMATIS PENGGUNAAN ORDINARY DAN ADVANCED KRIGING UNTUK PEMODELAN SPASIAL PADA DATA LINGKUNGAN
##plugins.themes.academic_pro.article.main##
Abstract
Metode Kriging merupakan salah satu teknik interpolasi geostatistik yang paling banyak digunakan dalam pemodelan spasial data lingkungan. Studi ini menyajikan tinjauan sistematis literatur terhadap 15 publikasi ilmiah yang menggunakan metode Ordinary dan Advanced Kriging, termasuk Universal Kriging, Co-Kriging, dan kombinasi dengan algoritma machine learning. Tujuan dari studi ini adalah untuk mengidentifikasi pola penggunaan, kelebihan, keterbatasan, serta tren penelitian dalam pemodelan spasial menggunakan Kriging. Hasil kajian menunjukkan bahwa Ordinary Kriging cocok untuk distribusi homogen, sementara Advanced Kriging memberikan hasil lebih akurat untuk data kompleks dan multivariat. Studi ini diharapkan menjadi referensi penting bagi peneliti di bidang lingkungan dan geospasial.
##plugins.themes.academic_pro.article.details##

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in JASCE.
The author holds the copyright of the submitted and published articles, with the understanding that articles are disseminated under the Creative Commons Attribution-NonCommercial 4.0.
The editor team is entitled to do the editing in accordance with the guidelines writing or template in the JASCE.
References
Crespo-Perez, V., Rahman, M., Abeywardana, A., & Ryu, D. (2022). Practical kriging models with divide and conquer algorithms for spatial prediction. Spatial Statistics, 49, 100565.
Fazal, S., Masood, A., Zia, H., & Khan, M. (2025). Field-scale spatial variability and uncertainty mapping of soil pH using ordinary kriging and cokriging. Environmental Monitoring and Assessment, 195(1), 1–17.
Hengl, T., Heuvelink, G. B., & Stein, A. (2007). Comparison of kriging and spline interpolation techniques for mapping soil pH properties. European Journal of Soil Science, 58(3), 523–533.
Jin, X., & Han, Y. (2021). Combination of machine learning and kriging for spatial estimation of environmental variables. Natural Resources Research, 30, 2653–2668.
Khosravi, K., Pourghasemi, H. R., Chapi, K., & Panahi, M. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755.
Kiran, R., Raju, K. D., & Kumar, N. (2021). Comparative suitability of ordinary kriging and IDW interpolation methods for groundwater level mapping in hard rock terrain. Groundwater for Sustainable Development, 12, 100552.
Li, W., Wang, H., Wang, J., & Zhang, Y. (2022). Prediction on spatial elevation using improved kriging algorithms: An experimental study. Expert Systems with Applications, 202, 117117.
Li, Z., & Heap, A. D. (2006). Optimization of sample patterns for universal kriging of environmental variables. Geoinformatica, 10, 283–305.
Liu, Y., Yu, M., & Liu, Y. (2024). An expanded spatial Durbin model with ordinary kriging of unobserved big climate data. Environmental Modelling & Software, 172, 105658.
Meng, Q., Cheng, Q., & Wang, W. (2015). Regression kriging of soil organic matter using the environmental variables derived from MODIS and DEM. Ecological Indicators, 54, 18–26.
Sun, W., Liu, Y., Yu, M., & Wang, L. (2016). Stream kriging: Incremental and recursive ordinary kriging over spatio-temporal data streams. Computers & Geosciences, 90, 78–90.
Wang, C., Xie, Y., & Zhang, X. (2017). Kriging with machine learning covariates in environmental sciences: A hybrid approach. Environmental Modelling & Software, 96, 145–157.
Xiong, Y., Li, J., & Xu, H. (2021). Random forest regression kriging modeling for soil organic carbon prediction in small watershed scale. Modeling Earth Systems and Environment, 10, 387–399.
Zhang, C., Zhan, X., & Shi, Z. (2017). Spatial prediction of soil organic matter using a hybrid geostatistical model of an extreme learning machine and ordinary kriging. Soil and Tillage Research, 165, 1–9.
Zhang, L., Song, X., & Liu, L. (2023). An enhanced integration of kriging with random forest for spatial prediction of groundwater quality. Environmental Research, 216, 114595.