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The Taiwan Water Resources Agency uses four techniques to monitor subsidence\udin Taiwan, namely data from leveling, global positioning system (GPS),\udmulti-level compaction monitoring wells (MCMWs), and interferometry\udsynthetic aperture radar (InSAR). Each data type has advantages and\uddisadvantages and is suitable for different analysis tools. Only MCMW data\udprovide compaction information at different depths in an aquifer system,\udthus they are adopted in this study. However, the cost of MCMW is high and\udthe number of MCMW is relatively low. Leveling data are thus also adopted\uddue to its high resolution and accuracy. MCMW data provide compaction\udinformation at different depths and the experimental data from the wells\udprovide the physical properties. These data are suitable for a physical\udmodel. Leveling data have high monitoring density in spatial domain but lack\udin temporal domain due to the heavy field work. These data are suitable for\uda black- or grey-box model. Poroelastic theory, which is known to be more\udconscientious than Terzaghi's consolidation theory, is adopted in this study\udwith the use of MCMW data. Grey theory, which is a widely used grey-box\udmodel, is adopted in this study with the use of leveling data. A fusion\udtechnique is developed to combine the subsidence predicted results from\udporoelastic and grey models to obtain a spatially and temporally connected\udtwo-dimensional subsidence distribution. The fusion model is successfully\udapplied to subsidence predictions in Changhua, Yunlin, Tainan, and Kaohsiung\udof Taiwan and obtains good results. A good subsidence model can help the\udgovernment to make the accurate strategies for land and groundwater resource\udmanagement

A fusion model used in subsidence prediction in Taiwan