DOI : https://doi.org/10.18517/ijaseit.12.6.16903

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

Budi Heru Santosa (1), Dwi Nowo Martono (2), Rachmadhi Purwana (3), Raldi Hendro Koestoer (4)
(1) School of Environmental Science, University of Indonesia, Kampus UI Salemba, Jl. Salemba Raya No. 4, Jakarta Pusat, 10430, Indonesia
(2) School of Environmental Science, University of Indonesia, Kampus UI Salemba, Jl. Salemba Raya No. 4, Jakarta Pusat, 10430, Indonesia
(3) School of Environmental Science, University of Indonesia, Kampus UI Salemba, Jl. Salemba Raya No. 4, Jakarta Pusat, 10430, Indonesia
(4) School of Environmental Science, University of Indonesia, Kampus UI Salemba, Jl. Salemba Raya No. 4, Jakarta Pusat, 10430, Indonesia
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How to cite (IJASEIT) :
Santosa, Budi Heru, et al. “Flood Vulnerability Evaluation and Prediction Using Multi-Temporal Data: A Case in Tangerang, Indonesia”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 6, Nov. 2022, pp. 2156-64, doi:10.18517/ijaseit.12.6.16903.
Citation Format :
Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used time-series land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy.

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