Mapping of Flood and Landslide Prone Areas using Composite Mapping Analysis Method Based on Geographic Information System in East Aceh

Disaster is an event that causes great losses to the community. Disasters are destructive, very detrimental, and require a long time to recover. To overcome the impact of natural disasters on the community in East Aceh Regency, research is needed related to the mapping system for multi-disaster prone areas (floods and landslides) in East Aceh Regency. The application used for the mapping process is ArcGIS Desktop and the research methodology used for mapping is Composite Mapping Analysis which consists of the process of determining the class of each parameter, determining the weight of each parameter by combining each parameter. The method of combining them consists of a scoring process for each parameter, then overlaying the parameters used, calculating and producing relative weights or spatial means, and combining spatial means to produce a value from the weight of each parameter for floods and landslides. The results of the study showed that the percentage of area for the class very prone to flood disasters was 232,156.13 Ha (42.3%), the vulnerable class had an area of 228,634.01 Ha (41.7%), and the non-vulnerable class had an area of 87,687.40 Ha (16%). The percentage of area for the class that is very vulnerable to landslides is 49,998.13 Ha (9.5%), the vulnerable class has an area of 301,863.93 Ha (57.2%), and the non-vulnerable class has an area of 175,542.56 Ha (33.3%). The contribution of this research is to provide information on disaster-prone areas, causal factors, characteristics of vulnerability to natural disasters such as floods and landslides and provide a basis for more effective decision-making in disaster mitigation and management efforts. This approach offers a new contribution to the technology of mapping and classifying disaster-prone areas.

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Publication Details

Journal
SISTEMASI
Published
2024-11-27
DOI
https://doi.org/10.32520/stmsi.v13i6.4483
Citations
1
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.78
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article

Mapping of Flood and Landslide Prone Areas using Composite Mapping Analysis Method Based on Geographic Information System in East Aceh

Taufiq Taufiq, Maya Maulita, Nurdin Nurdin
1 citations
SISTEMASI
Data Mining and Machine Learning Applications
0.78
article

Mapping of Flood and Landslide Prone Areas using Composite Mapping Analysis Method Based on Geographic Information System in East Aceh

Taufiq Taufiq, Maya Maulita, Nurdin Nurdin
article en
1 citations

Abstract

Disaster is an event that causes great losses to the community. Disasters are destructive, very detrimental, and require a long time to recover. To overcome the impact of natural disasters on the community in East Aceh Regency, research is needed related to the mapping system for multi-disaster prone areas (floods and landslides) in East Aceh Regency. The application used for the mapping process is ArcGIS Desktop and the research methodology used for mapping is Composite Mapping Analysis which consists of the process of determining the class of each parameter, determining the weight of each parameter by combining each parameter. The method of combining them consists of a scoring process for each parameter, then overlaying the parameters used, calculating and producing relative weights or spatial means, and combining spatial means to produce a value from the weight of each parameter for floods and landslides. The results of the study showed that the percentage of area for the class very prone to flood disasters was 232,156.13 Ha (42.3%), the vulnerable class had an area of 228,634.01 Ha (41.7%), and the non-vulnerable class had an area of 87,687.40 Ha (16%). The percentage of area for the class that is very vulnerable to landslides is 49,998.13 Ha (9.5%), the vulnerable class has an area of 301,863.93 Ha (57.2%), and the non-vulnerable class has an area of 175,542.56 Ha (33.3%). The contribution of this research is to provide information on disaster-prone areas, causal factors, characteristics of vulnerability to natural disasters such as floods and landslides and provide a basis for more effective decision-making in disaster mitigation and management efforts. This approach offers a new contribution to the technology of mapping and classifying disaster-prone areas.

SISTEMASIVol. 13(6)
Malikussaleh University (ID)
Openalex Percentile: Top 18%
Data Mining and Machine Learning Applications
0.78
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