Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia

• Autorzy: Ismanto Rido Dwi , Fitriana Hana Listi , Manalu Johanes , Purboyo Alvian Aji , Prasasti Indah

Identyfikatory

DOI

10.7494/geom.2023.17.6.129

• Języki publikacji: EN

Abstrakty

EN

Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.

Słowa kluczowe

EN

flood-susceptibility assessment; random forest; frequency ratio; Sumedang; remote sensing

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2023
• Tom: Vol. 17, no. 6
• Strony: 129--157
• Opis fizyczny: Bibliogr. 44 poz., tab., rys., wykr.

Twórcy

autor

Ismanto Rido Dwi

• National Research and Innovation Agency (BRIN), Research Center for Computing, Cibinong, Indonesia

autor

Fitriana Hana Listi

• National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia

• https://orcid.org/0000-0001-6752-8414

autor

Manalu Johanes

• National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia

• https://orcid.org/0009-0000-1761-3725

autor

Purboyo Alvian Aji

• Indonesia University of Education, Department of Geographic Information Science, Bandung, Indonesia

• https://orcid.org/0009-0009-3870-2327

autor

Prasasti Indah

• National Research and Innovation Agency (BRIN), Research Center for Remote Sensing, Pekayon, Indonesia

• https://orcid.org/0000-0001-5332-6061

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)