SVM-GAM Downscaling Framework for Quantifying Ecological Losses in Data-Limited Estuarine Dredging Areas

作者：Zijing Liu, Zhaoxing Han, Liguo Zhang, Dingkun Yin, jinxiang cheng, Ning Zhang, Shengqiang Liu, Chaohui Zheng, J Liu, Yan Li, Jinpeng Lv, Qi Liu, Junhui He · 发表于：Land · 年份：2026 · DOI：10.3390/land15071196 · 研究领域：Coastal wetland ecosystem dynamics、Environmental and Sediment Control、Coastal and Marine Management

Accurate quantification of ecological losses in estuarine environments is often hindered by the mismatch between coarse-resolution biological surveys and fine-scale physical disturbances from engineering activities. While numerical models can simulate high-resolution environmental shifts, the inherent sparsity of ecological monitoring points limits the precision of spatial impact assessments. This study develops an integrated spatial-downscaling framework to transform sparse monitoring data into a high-resolution spatial continuum. A three-tiered modeling approach was used: first, the estuarine domain was partitioned into five eco-hydrodynamic zones using an entropy-weighted Support Vector Machine (SVM); second, localized chained Generalized Additive Models (GAMs) were established within each zone using MIKE-simulated hydrodynamic and water-quality data as proxy drivers; and third, these localized response functions were propagated across the study area to quantify multi-trophic biomass and economic losses. The framework revealed substantial spatial non-stationarity. Dredging operations locally altered the estuarine hydrodynamic regime. In northern channels, decreases in flow velocity were statistically associated with phytoplankton biomass to decline by 5.0% to 23.42%. Conversely, southern velocity increases enhanced water exchange and plankton growth. Using silt curtains as a mitigation strategy reduced the loss of phytoplankton by 11.4% and zooplankton by 9.6%. As a result...