Abstract：
Land subsidence in Bangkok, a pressing environmental challenge, demands sustained long-term policy interventions. Although mitigation measures have successfully alleviated subsidence rates within inner Bangkok, neighboring provinces continue to experience escalating rates. Conventional land-based monitoring methods exhibit limitations in coverage, and the anticipated nonlinear contributions of climatic and socioeconomic factors further complicate the spatiotemporal distribution of subsidence. This study aims to provide future subsidence predictions for the near (2023-2048), mid (2049-2074), and far-future (2075-2100), employing Interferometric Synthetic Aperture Radar (InSAR), Random Forest machine learning algorithm, and combined Shared Socioeconomic Pathways-Representative Concentration Pathways (SSP-RCPs) scenarios to address these challenges. The mean Line-of-Sight (LOS) velocity was found to be -7.0 mm/year, with a maximum of -53.5 mm/year recorded in Ayutthaya. The proposed model demonstrated good performance, yielding an R2 value of 0.84 and exhibiting no signs of overfitting. Across all scenarios, subsidence rates tend to increase by more than -9.0 mm/year in the near-future. However, for the mid and far-future, scenarios illustrate varying trends. The \'only-urban-LU change\' scenario predicts a gradual recovery, while other change scenarios exhibit different tendencies.
摘要：
曼谷地面沉降，一个紧迫的环境挑战，需要持续的长期政策干预。尽管缓解措施已成功缓解了曼谷内的沉降率，邻近省份的利率继续上升。传统的陆基监测方法在覆盖范围方面存在局限性，气候和社会经济因素的预期非线性贡献进一步使沉降的时空分布复杂化。这项研究旨在为近期(2023-2048)提供未来沉降预测，中期（2049-2074），和遥远的未来（2075-2100），采用干涉合成孔径雷达(InSAR)，随机森林机器学习算法,并结合共享社会经济途径-代表性集中途径(SSP-RCP)方案来应对这些挑战。平均视线（LOS）速度为-7.0毫米/年，在大城府记录的最大-53.5毫米/年。所提出的模型表现出良好的性能，产生0.84的R2值，并且没有过拟合的迹象。在所有场景中，在不久的将来，沉降率往往会增加-9.0毫米/年以上。然而,对于中期和遥远的未来，场景说明了不同的趋势。“唯一的城市-LU变化”情景预测将逐步复苏，而其他变化情景表现出不同的趋势。
