Abstract: Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach-scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream-reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data-driven tool that can assist watershed managers with large quantities of complex, nonlinear data.
Abstract: Watershed managers and planners have long sought decision-making tools for forecasting changes in stream-channels over large spatial and temporal scales. In this research, we apply non-parametric, clustering and classification artificial neural networks to assimilate large amounts of disparate data types for use in fluvial hazard management decision-making. Two types of artificial neural networks (a counterpropagation algorithm and a Kohonen self-organizing map) are used in hierarchy to predict reach-scale stream geomorphic condition, inherent vulnerability and sensitivity to adjustments using expert knowledge in combination with a variety of geomorphic assessment field data. Seven hundred and eighty-nine Vermont stream reaches (+7500 km) have been assessed by the Vermont Agency of Natural Resources’ geomorphic assessment protocols, and are used in the development of this work. More than 85% of the reach-scale stream geomorphic condition and inherent vulnerability predictions match expert evaluations. The method’s usefulness as a QA/QC tool is discussed. The Kohonen self-organizing map clusters the 789 reaches into groupings of stream sensitivity (or instability). By adjusting the weight of input variables, experts can fine-tune the classification system to better understand and document similarities/differences among expert opinions. The use of artificial neural networks allows for an adaptive watershed management approach, does not require the development of site-specific, physics-based, stream models (i.e., is data-driven), and provides a standardized approach for classifying river network sensitivity in various contexts.
Abstract: Stream channel and watershed management requires an interdisciplinary approach by multiple stakeholders with often disparate goals and objectives. Even single management goals (e.g. mitigation of property loss due to stream bank erosion and flooding, restoring stream channels and aquatic species habitat) require solutions that span multiple spatial and temporal scales. To deal with the complexity associated with goals such as reducing fluvial erosion hazards, sediment and nutrient loading and threats to aquatic habitat associated with geomorphic instability, the Vermont Agency of Natural Resources' River Management Program has been developing and testing 1) protocols for conducting field-based and remote sensing data and 2) a geographical information system (GIS)-based tool since 1999. These protocols and tools facilitate an understanding of channel instability and assist in developing strategies at appropriate scales to restore channel equilibrium. A hierarchical system of data-driven artificial neural networks (ANNs) has been developed to enhance these existing GIS-based watershed management tools for predicting channel geomorphic condition and inherent sensitivity at the reach scale. We research and develop these ANNs as an alternative model that can incorporate large amounts of data for use in the operational management of watersheds. The ANNs have been trained and tested on remotely sensed and in-field data along with corresponding expert opinions of geomorphic condition and sensitivity. The focus is performing sensitivity analyses and addressing inadequacies associated with assimilation activities that are closely tied to large amounts of hydrological-relevant remote sensing and field data.