Rainfall Prediction using Backpropagation Method and GIS for Disaster Mitigation Mapping

Frequent weather or climate changes, coupled with the topography of Cirebon and Majalengka which are mostly lowlands and close to the sea, raise concerns that flood disasters can occur if mitigation is not done early. This research aims to predict and map rainfall and runoff discharge as a flood mitigation effort in the Cirebon and Majalengka areas. The method used in this research is the Backpropagation Method, which resembles the way the neuron system works in the human brain to learn patterns, optimize weights and biases, and reduce the error rate (mean square error) so that the predicted value is closer to the actual value. The results showed that Cirebon was predicted to have the highest rainfall of 2471 mm in 2048, based on prediction data from BMKG Penggung Station, while Majalengka was predicted to have the highest rainfall of 2922 mm in 2048, based on prediction data from BMKG Kertajati Station. The largest runoff discharge in the Cirebon region occurred in Susukan District (51.96 km²) with a discharge of 68.648 m³/second (Q25), and in the Majalengka region occurred in Lemahsugih District (78.64 km²) with a discharge of 104.16 m³/second (Q25). The implications of this research include mitigation efforts that can be carried out, such as reducing development above drainage channels, reforestation, maintaining open areas, reducing land use change, conservation of rivers and catchments, construction of flood embankments, and issuance of related regulations.

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Publication Details

Journal
Journal Transnational Universal Studies
Published
2024-10-19
DOI
https://doi.org/10.58631/jtus.v2i10.112
Citations
1
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.21
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article

Rainfall Prediction using Backpropagation Method and GIS for Disaster Mitigation Mapping

M. Toufiq Reza, Farhan Fadhilah, Awliya Tribhuwana
1 citations
Journal Transnational Universal Studies
Hydrological Forecasting Using AI
0.21
article

Rainfall Prediction using Backpropagation Method and GIS for Disaster Mitigation Mapping

M. Toufiq Reza, Farhan Fadhilah, Awliya Tribhuwana
article en
1 citations

Abstract

Frequent weather or climate changes, coupled with the topography of Cirebon and Majalengka which are mostly lowlands and close to the sea, raise concerns that flood disasters can occur if mitigation is not done early. This research aims to predict and map rainfall and runoff discharge as a flood mitigation effort in the Cirebon and Majalengka areas. The method used in this research is the Backpropagation Method, which resembles the way the neuron system works in the human brain to learn patterns, optimize weights and biases, and reduce the error rate (mean square error) so that the predicted value is closer to the actual value. The results showed that Cirebon was predicted to have the highest rainfall of 2471 mm in 2048, based on prediction data from BMKG Penggung Station, while Majalengka was predicted to have the highest rainfall of 2922 mm in 2048, based on prediction data from BMKG Kertajati Station. The largest runoff discharge in the Cirebon region occurred in Susukan District (51.96 km²) with a discharge of 68.648 m³/second (Q25), and in the Majalengka region occurred in Lemahsugih District (78.64 km²) with a discharge of 104.16 m³/second (Q25). The implications of this research include mitigation efforts that can be carried out, such as reducing development above drainage channels, reforestation, maintaining open areas, reducing land use change, conservation of rivers and catchments, construction of flood embankments, and issuance of related regulations.

Journal Transnational Universal StudiesVol. 2(10)
Universitas Swadaya Gunung Jati (ID)
Climate action
Openalex Percentile: Top 50%
Hydrological Forecasting Using AI
0.21
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