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https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/129230
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| 題名: | Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios |
| 作者: | Wei, Sun;Chang, Li-Chiu;Jie, Lin Jun;Chang, Fi-John |
| 日期: | 2026-02-07 |
| 上傳時間: | 2026-04-28 12:06:07 (UTC+8) |
| 出版者: | Elsevier B.V. |
| 摘要: | Drought increasingly threatens agricultural sustainability, particularly in groundwater-dependent regions where irrigation and aquifer recharge are closely linked. Taiwan's Zhuoshui River alluvial fan exemplifies this risk: long-term intensive pumping and rising climate extremes have amplified drought vulnerability. Yet most existing drought indices treat groundwater implicitly, and many AI studies focus on groundwater prediction without translating results into integrated vulnerability metrics. This study develops an AI-driven framework to assess future drought risk from climate, groundwater, and socio-environmental drivers. Groundwater level was predicted using a hybrid Convolutional Neural Network–Backpropagation model (CNN-BP) calibrated with 22 years of basin-wide gridded precipitation, temperature, and SPI data, together with groundwater levels from 18 monitoring wells. CNN-BP outperforms a BPNN benchmark, improving the correlation coefficient by 35.85% and reducing MAE by 19.51%, enabling robust projections for 2021–2100. These groundwater forecasts are then integrated with climatic (SPI), physiographic (soil, land use, elevation, slope, distance to river) and socio-economic (population) drivers to construct the Deep Learning-based Comprehensive Drought Vulnerability Indicator (DCDVI) under SSP1-2.6 and SSP5-8.5. Scenario results indicate consistent intensification of drought vulnerability relative to the historical baseline. SSP1-2.6 yields milder drought conditions and slower groundwater decline, while SSP5-8.5 leads to stronger drying and higher vulnerability. Under SSP5-8.5, highly vulnerable areas increase from 27.31% to 41.26% by 2081–2100. Overall, DCDVI provides a scalable, climate-responsive indicator that converts AI-based groundwater forecasts into actionable vulnerability maps. The framework provides a transferable decision-support tool for drought-prone, groundwater-reliant farming systems under climate change. |
| 關聯: | Groundwater for Sustainable Development 33 ,p. 101591 |
| DOI: | 10.1016/j.gsd.2026.101591 |
| 顯示於類別: | [人工智慧學系] 期刊論文
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