Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography

A river basin's flood-prone mapping is essential for managing flood risks, developing mitigation plans, and developing flood forecasting and warning systems, among other things. This research uses the HAND model to estimate the level of flood-prone and its distribution in watersheds. The method used is survey and image interpretation. The data used is DEM imagery with a resolution of 10 meters. Data analysis uses spatial analysis, which includes elevation, hydrological analysis, fill, flow accumulation, flow direction, flow distance, and minus statistical analysis. The results showed that the Serawai watershed has five classes: very prone, prone, moderate, not prone, and very not prone. The very prone class has an area of 112,213.82 ha (65.41%), including Tontang, Sedaha, Nanga Serawai, Begori, Nanga Lekawai, Surga, Buntut Ponte, and Nanga Segulang village. The prone class has an area of 29,356.65 ha (17.14%), spread across the village of part of Beurgea, part of Nanga Segulang, Nanga Jelundung, and part of Tontang village. The moderate level has an area of 18,971.52 ha (11.08%), spread across Tontang, part of Nanga Jelundung, and part of Baras Nabun village. The area with a not-prone is 7,996.20 ha (4.67%), spread across Baras Nabun and parts of Nanga Jelundung village. For areas that are very not prone, they have an area of 3,004.20 (1.75%), spread over parts of the villages of Sedaha, parts of Baras Nabun, and Nanga Jelundung. Based on the research results, it can be concluded that the HAND Model is an effective and easy-to-use model for estimating flood-prone areas.

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Publication Details

Journal
Indonesian Journal of Geography
Published
2024-12-03
DOI
https://doi.org/10.22146/ijg.89225
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography

Dony Andrasmoro, Ajun Purwanto, Eviliyanto Eviliyanto
Indonesian Journal of Geography
Computational Physics and Python Applications
article

Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography

Dony Andrasmoro, Ajun Purwanto, Eviliyanto Eviliyanto
article en

Abstract

A river basin's flood-prone mapping is essential for managing flood risks, developing mitigation plans, and developing flood forecasting and warning systems, among other things. This research uses the HAND model to estimate the level of flood-prone and its distribution in watersheds. The method used is survey and image interpretation. The data used is DEM imagery with a resolution of 10 meters. Data analysis uses spatial analysis, which includes elevation, hydrological analysis, fill, flow accumulation, flow direction, flow distance, and minus statistical analysis. The results showed that the Serawai watershed has five classes: very prone, prone, moderate, not prone, and very not prone. The very prone class has an area of 112,213.82 ha (65.41%), including Tontang, Sedaha, Nanga Serawai, Begori, Nanga Lekawai, Surga, Buntut Ponte, and Nanga Segulang village. The prone class has an area of 29,356.65 ha (17.14%), spread across the village of part of Beurgea, part of Nanga Segulang, Nanga Jelundung, and part of Tontang village. The moderate level has an area of 18,971.52 ha (11.08%), spread across Tontang, part of Nanga Jelundung, and part of Baras Nabun village. The area with a not-prone is 7,996.20 ha (4.67%), spread across Baras Nabun and parts of Nanga Jelundung village. For areas that are very not prone, they have an area of 3,004.20 (1.75%), spread over parts of the villages of Sedaha, parts of Baras Nabun, and Nanga Jelundung. Based on the research results, it can be concluded that the HAND Model is an effective and easy-to-use model for estimating flood-prone areas.

Indonesian Journal of GeographyVol. 56(3)
Universitas Muhammadiyah Pontianak (ID), Pancur Kasih Association (ID)
Direktorat Jenderal Pendidikan Tinggi
Openalex Percentile: Top 78%
Computational Physics and Python Applications
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