A comparison of different GIS-based interpolation methods for bathymetric data: case study of Bawean Island, East Java
Abstract
The bottom surface’s portrayal is crucial in many different practices. Therefore, accurate bathymetry data is required. The interpolation method is one element that influences the accuracy of a Single Beam Echosounder’s depth data. IDW, Kriging, and TIN are three standard interpolation techniques. This study compares these three methods with two scenarios utilizing the spatial analysis to establish the most effective technique for producing the digital elevation model of the seafloor beneath Bawean Island. The IDW exhibits the strongest R-squared (0.9998779 in Scenario-1 and 0.9999875 in Scenario-2) and correlation (0.9998796 in Scenario-1 and 0.9999595 in Scenario-2). It indicates that IDW and bathymetric data have the closest relationships. IDW has the lowest error, as measured by the MAE value (0.02 in Scenario-1 and 0.009 in Scenario-2), followed in both cases by Kriging and TIN. Additionally, the RMSE for IDW shows the same outcome (0.045 in Scenario 1 and 0.016 in Scenario 2). In the meantime, comparing the first and second scenarios reveals that the second, which has fewer data, is preferable to the first. Since the MAE and RMSE in the first scenario are greater than those in the second, we may infer that more data leads to more significant errors.
Keyword : SBES, interpolation methods, IDW, Kriging, TIN, spatial analysis
How to Cite
Pratomo, D. G., Safira, R. A. D., & Stefani, O. (2023). A comparison of different GIS-based interpolation methods for bathymetric data: case study of Bawean Island, East Java. Geodesy and Cartography, 49(4), 186–194. https://doi.org/10.3846/gac.2023.18250
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Curtarelli, M., Leão, J., Ogashawara, I., Lorenzzetti, J., & Stech, J. (2015). Assessment of spatial interpolation methods to map the bathymetry of an Amazonian hydroelectric reservoir to aid in decision making for water management. ISPRS International Journal of Geo-Information, 4(1), 220–235. https://doi.org/10.3390/ijgi4010220
Ferreira, I. O., Rodrigues, D. D., Dos Santos, G. R., & Rosa, L. M. F. (2017). Em superficies batimétricas: IDW ou krigagem? Boletim de Ciencias Geodesicas, 23(3), 493–508. https://doi.org/10.1590/S1982-21702017000300033
Guo, Q., Li, W., Yu, H., & Alvarez, O. (2010). Effects of topographic variability and lidar sampling density on several DEM interpolation methods. Photogrammetric Engineering & Remote Sensing, 76(6), 701–712. https://doi.org/10.14358/PERS.76.6.701
Hamdy, O., Gaber, H., Abdalzaher, M. S., & Elhadidy, M. (2022). Identifying exposure of urban area to certain seismic hazard using machine learning and GIS: A case study of Greater Cairo. Sustainability, 14(17), 10722. https://doi.org/10.3390/SU141710722
Hell, B., Broman, B., Jakobsson, L., Jakobsson, M., Magnusson, Å., & Wiberg, P. (2012). The use of bathymetric data in society and science: A review from the Baltic Sea. Ambio, 41(2), 138–150. https://doi.org/10.1007/s13280-011-0192-y
Henrico, I. (2021). Optimal interpolation method to predict the bathymetry of Saldanha Bay. Transactions in GIS, 25(4), 1991–2009. https://doi.org/10.1111/tgis.12783
Hossen, I., Anders, M. A., Wang, L., & Adam, G. C. (2022). Data-driven RRAM device models using Kriging interpolation. Scientific Reports, 12(1), 1–12. https://doi.org/10.1038/s41598-022-09556-4
Hu, J. (1995, May). Methods of generating surfaces in environmental GIS applications [Conference presentation]. ESRI User Conference Proceedings, San Diego. https://proceedings.esri.com/library/userconf/proc95/to100/p089.html
Jones, C. B., Kidner, D. B., & Ware, J. M. (1994). The implicit triangulated irregular network and multiscale spatial databases. The Computer Journal, 37(1), 43–57. https://doi.org/10.1093/comjnl/37.1.43
Kartal, S. K., Hacıoğlu, R., Görmüş, K. S., Kutoğlu, H., & Leblebicioğlu, M. K. (2022). Modeling and analysis of sea-surface vehicle system for underwater mapping using single-beam echosounder. Journal of Marine Science and Engineering, 10(10), 1349. https://doi.org/10.3390/JMSE10101349
Karunasingha, D. S. K. (2022). Root mean square error or mean absolute error? Use their ratio as well. Information Sciences, 585, 609–629. https://doi.org/10.1016/j.ins.2021.11.036
Liu, H., & Wu, C. (2019). Developing a scene-based triangulated irregular network (TIN) technique for individual tree crown reconstruction with LiDAR data. Forests, 11(1), 28. https://doi.org/10.3390/f11010028
Liu, Z., & Yan, T. (2021). Comparison of spatial interpolation methods based on ArcGIS. Journal of Physics: Conference Series, 1961(1), 012050. https://doi.org/10.1088/1742-6596/1961/1/012050
Lu, Y., Song, W., Ro, Y., & Yoo, C. (2022). Numerical experiments applying simple kriging to intermittent and log-normal data. Water, 14(9), 1364. https://doi.org/10.3390/W14091364
Mohammad Sham, N., Anual, Z. F., & Shaharudin, R. (2022). GIS based interpolation method to urinary metal concentrations in Malaysia. Food and Chemical Toxicology, 163, 112949. https://doi.org/10.1016/j.fct.2022.112949
Murphy, R. R., Curriero, F. C., & Ball, W. P. (2010). Comparison of spatial interpolation methods for water quality evaluation in the Chesapeake Bay. Journal of Environmental Engineering, 136(2), 160–171. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000121
Parente, C., & Vallario, A. (2019). Interpolation of single beam echo sounder data for 3D bathymetric model. International Journal of Advanced Computer Science and Applications, 10(10), 2019. https://doi.org/10.14569/IJACSA.2019.0101002
Rebekić, A., Lončarić, Z., Petrović, S., & Marić, S. (2015). Pearson’s or spearman’s correlation coefficient - which one to use? Poljoprivreda, 21(2), 47–54. https://doi.org/10.18047/poljo.21.2.8
Šiljeg, A., Lozić, S., & Šiljeg, S. (2015). A comparison of interpolation methods on the basis of data obtained from a bathymetric survey of Lake Vrana, Croatia. Hydrology and Earth System Sciences, 19(8), 3653–3666. https://doi.org/10.5194/hess-19-3653-2015
Solikin, S., Manik, H. M., Pujiyati, S., & Susilohadi, S. (2018). Measurement of bottom backscattering strength using single-beam echosounder. Journal of Physics: Conference Series, 1075, 012036. https://doi.org/10.1088/1742-6596/1075/1/012036
Sukkuea, A., & Heednacram, A. (2022). Prediction on spatial elevation using improved kriging algorithms: An application in environmental management. Expert Systems with Applications, 207, 117971. https://doi.org/10.1016/J.ESWA.2022.117971
Tasri, A. (2022). Inverse distance interpolation for used in unstructured mesh finite volume solver. Journal of Applied Engineering Science, 20(2), 597–601. https://doi.org/10.5937/jaes0-34022
Twomey, P. J., & Kroll, M. H. (2008). How to use linear regression and correlation in quantitative method comparison studies. International Journal of Clinical Practice, 62(4), 529–538. https://doi.org/10.1111/J.1742-1241.2008.01709.X
Yang, Y., Hui, L., Ran, X., Liu, M., Yang, L., & Zhou, Y. (2018). Application of sonar equation in the design of ocean instruments. In Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018) (pp. 186–192). Atlantis Press. https://doi.org/10.2991/cecs-18.2018.34
Zhang, Y., Yu, W., & Zhu, D. (2022). Terrain feature-aware deep learning network for digital elevation model superresolution. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 143–162. https://doi.org/10.1016/J.ISPRSJPRS.2022.04.028