Validating the GIS-based Flood Susceptibility Model Using Synthetic Aperture Radar (SAR) Data in Sengah Temila Watershed, Landak Regency, Indonesia

Ajun Purwanto(1*), Dony Andrasmoro(2), Eviliyanto Eviliyanto(3), Rustam Rustam(4), Mohd Hairy Ibrahim(5), Arif Rohman(6)

(1) IKIP PGRI Pontianak, Jl. Ampera No. 88, Kota Baru Pontianak, Kalimantan Barat 78116, Indonesia
(2) IKIP PGRI Pontianak, Jl. Ampera No. 88, Kota Baru Pontianak, Kalimantan Barat 78116, Indonesia
(3) IKIP PGRI Pontianak, Jl. Ampera No. 88, Kota Baru Pontianak, Kalimantan Barat 78116, Indonesia
(4) IKIP PGRI Pontianak, Jl. Ampera No. 88, Kota Baru Pontianak, Kalimantan Barat 78116, Indonesia
(5) Department Geography & Environment, Faculty Human Sciences Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
(6) School of Geography, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, United Kingdom
(*) Corresponding Author


In Indonesia, especially in regions where natural conditions and human activity coexist, flood disasters are a strong possibility. Flooding regularly has an impact on Sengah Temila, which is a component j/ of Indonesia's West Kalimantan Province. The issue in Sengah Temila is that there is little knowledge of the distribution of flood susceptibility in this region. The GIS-based flood susceptibility model has been widely used in Indonesia, but research dedicated to validating the model is limited. SAR-based analysis has been used for flood mapping in Indonesia, but its use for validating flood models has been limited.  The objective of this study is to identify the optimal weighting scenario for a GIS-based multi-criteria analysis flood model for use in the Sengah Temila Watershed. The GIS-based model is created by merging spatial parameters, including slope, elevation, flow accumulation, drainage density, land use and land cover (LULC), soil type, normalized difference vegetation index (NDVI), curvature, rainfall, distance to river, and topographic wetness index (TWI) with weighted multi-criteria analysis. In addition, Sentinel-1 GRD images from before and after the floods have been retrieved from Google Earth Engine using past floods of the watershed. In order to create a SAR-based flood model, the researchers then integrated and categorized the results. Eleven weighting scenarios were used to create eleven GIS-based flood models. To calculate the degree of spatial similarity, all of these models were contrasted with the SAR-based model using the Fuzzy Kappa approach. We found that in order to achieve ideal weighting, slope, topographic wetness index (TWI), rainfall, and flow accumulation should each be given a larger value.

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