Fig 1.
The workflow of this study.
Fig 2.
Location and topographic characteristics of the Boluo watershed in the Dongjiang River Basin, southern China.
Digital Elevation Model (DEM) data were obtained from the Shuttle Radar Topography Mission (SRTM) at 90-meter resolution. Administrative boundary data were obtained from the Standard Map Service of the Ministry of Natural Resources of China (http://bzdt.ch.mnr.gov.cn/). All these datasets are publicly accessible (S1 File).
Fig 3.
Correlation Matrix of 19 Typhoon Features.
Table 1.
Comparison of Sensitivity to Enhanced Typhoon Features and Training-Inference Performance Among Four Models (LR, ANN, RF and XGB).
Fig 4.
Overall model performance comparison across three feature engineering scenarios in the Boluo watershed.
Fig 5.
Seasonal performance comparison of four models under Enhanced Typhoon scenario.
Fig 6.
Model performance stratified by flow magnitude ranges.
(a) RMSE across six magnitude categories showing exponential growth from low to extreme flows; (b) Sample distribution; (c) Normalized NSE versus normalized RMSE performance map. Normalization is performed within each magnitude category to enable cross-range.
Fig 7.
Flood period performance evaluation through peak prediction and event detection.
(a) Box plots of peak prediction relative errors while negative values indicating underestimation. (b) Confusion matrices for binary flood detection (P90 threshold). Color intensity represents normalized rates, with green indicating correct classification and red indicating misclassification.
Fig 8.
Model performance across three typhoon distance scenarios during extreme flood events: (a) Near-distance typhoon (55 km, June 28, 2008), (b) Medium-distance typhoon (327 km, July 17, 2006), and (c) Far-distance typhoon (445 km, August 23, 2007).
Left panels show temporal evolution of observed (black dots) and predicted streamflow with precipitation (blue bars, right y-axis). Right panels display probability density distributions of streamflow predictions.
Fig 9.
SHAP-based feature importance analysis for (a) aggregated global importance and (b) feature contributions during the maximum flood event.
Fig 10.
Physics-informed typhoon feature engineering for (a) sigmoid versus linear distance-impact function comparison, (b) feature-streamflow correlations during typhoon events, and (c) case study demonstrating cumulative typhoon impact on extreme flood generation.