Spatial Data Mining for Prediction of Unobserved Zinc Pollutants Using Various Kriging Methods

Document Type : Original Research

Authors
D. P. Krotha
S. Fathimabi
G. JayaLakshmi
M. Suneetha
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada-520007, India
10.22034/jaert.1.3.57
Abstract
Following years of contamination, rivers may experience sig­nificant levels of heavy metal pollution. Our research aims to pinpoint hazardous areas in these rivers. In our specific case, we focus on the floodplains of the Meuse River contaminated with zinc (Zn). Elevated zinc concentrations can lead to various health issues, including anemia, rashes, vomiting, and stomach cramping. However, due to limited sample data on zinc con­centrations in the Meuse River, it becomes imperative to gen­erate missing data in unidentified regions. This study employs universal Kriging in spatial data mining to investigate and pre­dict unknown zinc pollutants. The semivariogram serves as a valuable tool for illustrating the variability pattern of zinc. To predict concentrations in unknown regions, the model captured is interpolated using the Kriging method. Employing regression with geographic weighting allows us to observe how stimu­lus-response relationships change spatially. Various semivario­gram models, such as Matern, exponential, and linear, are uti­lized in our work. Additionally, we introduce Universal Kriging and geographically weighted regression. Experimental findings indicate that: (i) the Matern model, determined by calculating the minimum error sum of squares, is the most suitable theoret­ical semivariogram model; and (ii) the accuracy of predictions is visually demonstrated by projecting results onto a real map.

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Gunawan, A. A., Falah, A. N., Faruk, A., Lutero, D. S., Ruchjana, B. N., & Abdullah, A. S. (2016, October). “Spatial data mining for predicting of unobserved zinc pollutant using ordinary point Kriging”. In 2016 International Workshop on Big Data and Information Security (IWBIS), IEEE, 83-88. https://doi.org/10.1109/IWBIS.2016.7872894.
Falah, A. N., Hamid, N., Rusyaman, E., Abdullah, A. S., & Ruchjana, B. N. (2021). “Implementation of Ordinary Co-Kriging method for prediction of coal quality variable at unobserved locations”. In Journal of Physics: Conference Series (Vol. 1722, No. 1, p. 012076). IOP Publishing.
Ahn, S., Ryu, D. W., & Lee, S. (2020). “A machine learning-based approach for spatial estimation using the spatial features of coordinate information”. ISPRS International Journal of Geo-Information, Mdpi, 9(10), 587. https://doi.org/10.3390/ijgi9100587
Behrens, T., & Viscarra Rossel, R. A. (2020). “On the interpretability of predictors in spatial data science: The information horizon”. Scientific Reports, 10(1), 16737. https://doi.org/10.1038/s41598-020-73773-y
Paramasivam, C. R., & Venkatramanan, S. (2019). “An introduction to various spatial analysis techniques”. GIS and geostatistical techniques for groundwater science, Elsevier, 23-30. https://doi.org/10.1016/B978-0-12-815413-7.00003-1
Jiang, Z. (2018). “A survey on spatial prediction methods”. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1645-1664.https://doi.org/10.1109/tkde.2018.2866809
Falah, A. N., Abdullah, A. S., Parmikanti, K., & Ruchjana, B. N. (2017, March). “Prediction of cadmium pollutant with ordinary point kriging method using GStat-R”. In AIP Conference Proceedings (Vol. 1827, No. 1). AIP Publishing.
Hussain, M. R. (2016). “An Overview of Geographic Information System (GIS)”, Researchgate. https://doi.org/10.13140/RG.2.1.3569.5603
Choudhury, N. H., Rahman, A., & Ferdousi, S. (2015). “Kriging infill of missing data and temporal analysis of rainfall in North Central region of Bangladesh. J. Climatol. Weather Forecast”, Researchgate, vol. 3, 1-5. https://doi.org/10.4172/2332-2594.1000141
Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons.
Montero, J. M., Fernández-Avilés, G., & Mateu, J. (2015). “Spatial and spatio-temporal geostatistical modeling and kriging”. John Wiley & Sons.
Setiawan, A., & Rosadi, R. (2011). “Spasial Data Mining menggunakan Model SAR-Kriging”. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 5(3), 52-61. https://doi.org/10.22146/ijccs.5213
Shekhar, S., Evans, M. R., Kang, J. M., & Mohan, P. (2011). “Identifying patterns in spatial information: A survey of methods”. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 193-214. https://doi.org/10.1002/widm.25
Goovaerts, P. (2009). “AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences”. Computers & geosciences, Elsevier, 35(6),1255-1270. https://doi.org/10.1016/j.cageo.2008.08.014
Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). “Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis”. Geoderma, 146(1-2), 102-113.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). “The elements of statistical learning: data mining, inference, and prediction” (Vol. 2, pp. 1-758). New York: springer.
Oliver, M. A., & Webster, R. (2007). “Kriging: a method of interpolation for geographical information systems”. International Journal of Geographical Information System, 4(3), 313-332. https://doi.org/10.1080/02693799008941549
Hengl, T., Heuvelink, G. B., & Stein, A. (2004). “A generic framework for spatial prediction of soil variables based on regression-kriging”. Geoderma, 120(1-2), 75-93.
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). “Integration of remote sensing data and GIS for accurate mapping of flooded areas”. International Journal of Remote Sensing, 23(3), 429-441.
Middelkoop, H. (2000). “Heavy-metal pollution of the river Rhine and Meuse floodplains in the Netherlands”. Netherlands journal of geosciences, 79(4), 411-427. https://doi.org/10.1017/S0016774600021910.
Journal of Advanced Environmental Research and Technology
Volume 1, Issue 3 - Serial Number 3
September 2023
Pages 57-67