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Integrating CMIP6 and Remote Sensing Datasets for Current and Future Flood Susceptibility Projections Using Machine Learning Under Climate Change Scenarios in Demak District for Future Sustainable Planning

Author

Listed:
  • Aprizal Verdyansyah

    (The Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Jl. Angkasa I No. 2 Kemayoran, Jakarta Pusat 10720, Indonesia
    Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan)

  • Yi-Ling Chang

    (Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan)

  • Fu-Cheng Wang

    (Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan)

  • Fuan Tsai

    (Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan)

  • Tang-Huang Lin

    (Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan
    Center for Astronautical Physics and Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist, Taoyuan City 32001, Taiwan
    Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung 807378, Taiwan)

Abstract

Among various natural hazards, floods stand out due to their frequency and severe impact on society and the environment. This study aimed to develop a flood susceptibility model for Demak District, Indonesia, by integrating remote sensing data, machine learning techniques, and CMIP6 Global Climate Model (GCM) data. The approach involved mapping current flood susceptibility using Sentinel-1 SAR data as the flood inventory and applying machine learning algorithms such as MLP-NN, Random Forest, Support Vector Machine (SVM), and XGBoost to predict flood-prone areas. Additionally, future flood susceptibility was projected using CMIP6 GCM precipitation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) covering the 2021–2100 period. To enhance the reliability of future projections, a multi-model ensemble approach was employed by combining the outputs of multiple GCMs to reduce model uncertainties. The results showed a significant increase in flood susceptibility, especially under higher emission scenarios (SSP5-8.5), with very high susceptibility areas growing from 16.67% in the current period to 27.43% by 2081–2100. The XGBoost model demonstrated the best performance in both current and future projections, providing valuable sustainable planning insights for flood risk management and adaptation to climate change.

Suggested Citation

  • Aprizal Verdyansyah & Yi-Ling Chang & Fu-Cheng Wang & Fuan Tsai & Tang-Huang Lin, 2025. "Integrating CMIP6 and Remote Sensing Datasets for Current and Future Flood Susceptibility Projections Using Machine Learning Under Climate Change Scenarios in Demak District for Future Sustainable Planning," Sustainability, MDPI, vol. 17(18), pages 1-35, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8188-:d:1747261
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    References listed on IDEAS

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