Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China

摘要: Identifying priority zones for river restoration is important biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led research better understand ecological status within a develop targeted planning strategy restoration. To address this need, coupling hydrological machine learning models were constructed identify based on dataset aquatic organisms (i.e., algae, macroinvertebrates, fish) physicochemical indicators that collected from 130 sites in September 2014 Taizi River, northern China. A process-based model soil water assessment tool (SWAT) was developed temporal-spatial variations environmental indicators. support vector (SVM) applied explore relationships between Biological indices among different periods simulated by SWAT SVM models. Results indicated biological exhibited apparent temporal spatial patterns, those patterns more evident upper reaches compared lower reaches. The River flood season than dry season. Priority identified seasons setting target values biota organisms, results suggest conditions significantly influenced prioritization over other parameters. Our approach could be seasonal ecosystems provide preferences