Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data
Keywords:
Geographic Information System, Tsunami, Machine Learning
Abstract
The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.
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References
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[2] S. P. D. Sriyanto, P. A. Angmalisang, and L. Manu, “Optimal Tide Gauge Location for Tsunami Validation in The Lembeh Island, North Sulawesi,” Indones. J. Geosci., vol. 9, no. 3, pp. 315–327, 2022, doi: 10.17014/ijog.9.3.315-327.
[3] A. D. Permatasari and S. Y. Joko Prasetyo, “Identifikasi Wilayah Resiko Kerusakan Lahan Terbangun Sebagai Dampak Tsunami Berdasarkan Analisis Building Indices,” J. Transform., vol. 20, no. 1, p. 13, Jul. 2022, doi: 10.26623/transformatika.v20i1.5209.
[4] A. Y. Isnaeni and S. Y. J. Prasetyo, “Klasifikasi Wilayah Potensi Risiko Kerusakan Lahan Akibat Bencana Tsunami Menggunakan Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 1, pp. 33–42, 2022, doi: 10.28932/jutisi.v8i1.4056.
[5] J. Keller, Tsunami Modeling and Hazard Assessment. New York: States Academic Pr, 2022.
[6] I. R. Pranantyo, M. Heidarzadeh, and P. R. Cummins, “Complex tsunami hazards in eastern Indonesia from seismic and non-seismic sources: Deterministic modelling based on historical and modern data,” Geosci. Lett., vol. 8, no. 1, pp. 1–16, Dec. 2021, doi: 10.1186/s40562-021-00190-y.
[7] A. S. Alademomi, C. J. Okolie, O. E. Daramola, R. O. Agboola, and T. J. Salami, “Assessing the relationship of LST, NDVI and EVI with land cover changes in the Lagos Lagoon environment,” Quaest. Geogr., vol. 39, no. 3, pp. 87–109, 2020, doi: 10.2478/quageo-2020-0025.
[8] M. Dangulla, L. A. Munaf, and F. R. Mohammad, “Spatio-temporal analysis of land use/land cover dynamics in Sokoto Metropolis using multi-temporal satellite data and Land Change Modeller,” Indones. J. Geogr., vol. 52, no. 3, pp. 306–316, 2021, doi: 10.22146/IJG.46615.
[9] T. H. Rehman, M. E. Lundy, and B. A. Linquist, “Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems,” Remote Sens., vol. 14, no. 12, pp. 1–18, 2022, doi: 10.3390/rs14122770.
[10] S. Y. J. Prasetyo, B. H. Simanjuntak, K. D. Hartomo, and W. Sulistyo, “Computer model for tsunami vulnerability using sentinel 2a and srtm images optimized by machine learning,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2821–2835, 2021, doi: 10.11591/eei.v10i5.3100.
[11] T. R. Fariz, F. Daeni, and H. Sultan, “Pemetaan Perubahan Penutup Lahan Di Sub-DAS Kreo Menggunakan Machine Learning Pada Google Earth Engine,” J. Sumberd. Alam dan Lingkung., vol. 8, no. 2, pp. 85–92, 2021, doi: 10.21776/ub.jsal.2021.008.02.4.
[12] T. Wahyono, Fundamental of Python for Machine Learning, Cetakan 1. Yogyakarta: Penerbit Gava Media, 2018.
[13] M. Aldi, I. R. Siregar, and A. Bilqis, “Pemetaan Daerah Rawan Longsor Menggunakan Machine Learning di Kecamatan Muara Tami, Kota Jayapura, Papua,” J. Geofis., vol. 19, no. 1, pp. 24–30, 2021.
[14] D. Indrajaya, A. Setiawan, and B. Susanto, “Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification,” Matrik J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 22, no. 1, pp. 149–164, 2022, doi: https://doi.org/10.30812/matrik.v22i1.1758.
[15] D. Y. Heryadi and T. Wahyono, Machine Learning Konsep dan Implementasi, Cetakan 1. Yogyakarta: Penerbit Gava Media, 2020.
[16] S. Efendi and P. Sihombing, “Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 3, pp. 583–594, Jul. 2022, doi: 10.30812/matrik.v21i3.1898.
[17] M. Trishiani, S. Sugianto, T. Arabia, and M. Rusdi, “Vegetation density analysis in Banda Aceh city before and after the tsunami using satellite imagery data,” in IOP Conference Series: Earth and Environmental Science, Jan. 2022, vol. 951, no. 1, pp. 1–8. doi: 10.1088/1755-1315/951/1/012073.
[18] S. Koshimura, L. Moya, E. Mas, and Y. Bai, “Tsunami damage detection with remote sensing: A review,” Geosci., vol. 10, no. 5, pp. 1–28, 2020, doi: 10.3390/geosciences10050177.
[19] A.-L. Balogun, S. T. Yekeen, B. Pradhan, and O. F. Althuwaynee, “Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models,” Remote Sens., vol. 12, no. 7, pp. 1–25, Apr. 2020, doi: 10.3390/rs12071225.
[20] A. Andriani, G. D. Putra, S. Ramadhani, Ismael, and H. G. Putra, “Analysis of landslide potential due to changes of land use/land cover at the Kuranji watershed, Padang using normalized difference built-up index (NDBI),” in International Conference on Disaster Mitigation and Management (ICDMM 2021), Dec. 2021, pp. 1–6. doi: 10.1051/e3sconf/202133103007.
[21] T. N. Nuklianggraita, A. Adiwijaya, and A. Aditsania, “On the Feature Selection of Microarray Data for Cancer Detection based on Random Forest Classifier,” J. Infotel, vol. 12, no. 3, pp. 89–96, 2020, doi: 10.20895/infotel.v12i3.485.
[22] D. Kurniadi, A. Mulyani, and I. Muliana, “Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire,” J. Online Inform., vol. 6, no. 1, pp. 53–62, Jun. 2021, doi: 10.15575/join.v6i1.701.
[23] N. Hasdyna, B. Sianipar, and E. M. Zamzami, “Improving The Performance of K-Nearest Neighbor Algorithm by Reducing The Attributes of Dataset Using Gain Ratio,” in Journal of Physics: Conference Series, Jun. 2020, pp. 1–6. doi: 10.1088/1742-6596/1566/1/012090.
[24] N. R. Wardani, S. Saepudin, and C. Warman, “Sentimen Analisis Kegiatan Trading Pada Ap- likasi Twitter dengan Algoritma SVM , KNN Dan Random Forrest,” J. Sains Komput. Inform., vol. 6, no. 2, pp. 863–870, 2022, doi: http://dx.doi.org/10.30645/j-sakti.v6i2.497.
[25] S. Zarco-Perello and N. Simões, “Ordinary kriging vs inverse distance weighting: Spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico,” PeerJ, vol. 2017, no. 11, 2017, doi: 10.7717/peerj.4078.
[26] N. Elizabeth Michael, M. Mishra, S. Hasan, and A. Al-Durra, “Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique,” Energies, vol. 15, no. 6, pp. 1–20, Mar. 2022, doi: 10.3390/en15062150.
[27] T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.
[28] S. M. Robeson and C. J. Willmott, “Decomposition of the mean absolute error (MAE) into systematic and unsystematic components,” PLoS One, vol. 18, no. 2, pp. 1–8, Feb. 2023, doi: 10.1371/journal.pone.0279774.
[29] Y. Yamanaka and T. Shimozono, “Tsunami inundation characteristics along the Japan Sea coastline: effect of dunes, breakwaters, and rivers,” Earth, Planets Sp., vol. 74, no. 1, pp. 1–16, Dec. 2022, doi: 10.1186/s40623-022-01579-5.
[30] V. Zuzulová, J. Vido, and B. Šiška, Agricultural Drought in Slovakia: An Impact Assessment NDVI and Satellite Based Data. Switzerland: Springer Cham, 2020.
[31] G. Cárdenas and P. A. Catalán, “Accelerating Tsunami Modeling for Evacuation Studies through Modification of the Manning Roughness Values,” GeoHazards, vol. 3, no. 4, pp. 492–508, 2022, doi: 10.3390/geohazards3040025.
[32] S. Sharifzadeh and S. Adhikari, “A Support Vector Machine-Based Water Detection Analysis in a Heterogeneous Landscape Using Landsat TM Imagery,” Calif. Geogr., vol. 59, pp. 1–22, 2020.
[33] F. A. Safira, C. Muryani, and G. A. Tjahjono, “Tsunami Susceptibility Analysis in Coastal Area Petanahan District, Kebumen Regency,” Jambura Geosci. Rev., vol. 4, no. 2, pp. 110–122, 2022, doi: 10.34312/jgeosrev.v4i2.13938.
[34] R. Paulik, H. Craig, and B. Popovich, “A national-scale assessment of population and built-environment exposure in Tsunami evacuation zones,” Geosci., vol. 10, no. 8, pp. 1–15, 2020, doi: 10.3390/geosciences10080291.
[35] S. M. RAHAYU and A. S. ANDINI, “Study of Tsunami Mitigation Based on Vegetation in Serenting Beach, Mandalika Special Economic Zone, Lombok Island,” Int. J. Res. -GRANTHAALAYAH, vol. 8, no. 12, pp. 60–68, 2020, doi: 10.29121/granthaalayah.v8.i12.2020.2473.
[36] Y. Zhao, P. Hou, J. Jiang, J. Zhao, Y. Chen, and J. Zhai, “High-Spatial-Resolution NDVI Reconstruction with GA-ANN,” Sensors, vol. 23, no. 4, p. 2040, Feb. 2023, doi: 10.3390/s23042040.
[37] M. Sarafanov, E. Kazakov, N. O. Nikitin, and A. V. Kalyuzhnaya, “A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and ndvi,” Remote Sens., vol. 12, no. 23, pp. 1–21, 2020, doi: 10.3390/rs12233865.
[38] F. H. Evans and J. Shen, “Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning,” Remote Sens., vol. 13, no. 13, pp. 1–20, Jun. 2021, doi: 10.3390/rs13132435.
Published
2023-03-28
How to Cite
wibowo, G., Prasetyo, S., & Sembiring, I. (2023). Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 365-380. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2677
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