Background

1. Development of a sediment entrainment predictive tool for river management purposes Baker Environmental Hydraulics Laboratory, Hancock Hall. e-mail: mvalyrak@vt.edu M. Valyrakis (CEE), P. Diplas (CEE), C. L. Dancey (ME) Background Sedimentation and erosionof natural streams are important processes which affect their morphology and ecological health in many ways. Issues such as the spawning habitat and growth of fish, along with water quality are significantly influenced by aggregation and degradation phenomena typically occurring in rivers. For instance, the transport of sediments, with or without contaminants adsorbed on them, by the channel flow may pose a threat to the various uses of stream flow (e.g. irrigation). Human intervention on riverine ecosystems such as control of flow rates released by dams, disposal of pollutants in rivers and urbanization actions may endanger this sensitive environment. Thus there exists a need for a technique which will predict the conditions under which incipient motion of contaminated sediments occurs and will guide hydraulic engineers in taking the right action and reduce the risk of negatively impacting rivers due to poor planning. Such a model may be effectively combined in the future with statistical tools to aid decision making in stream restoration projects and implementation of sustainable practices in riverine ecosystems. Scope of current study Experimental Setup Modeling technique Results Conclusions The proposed model Limitations Future work Different Adaptive Neuro-Fuzzy Inference Systems (ANFIS) architectures are produced and trained with data obtained from flume experiments performed at the Baker Environmental Hydraulics Laboratory. The aforementioned hybrid model, which is based on neural learning and fuzzy inference principles, attempts to predict the critical flow conditions above which erosion processes commence. • Has the ability to generalize the threshold of motion criterion despite the complexity of the physical system • Is data driven so no need for prior knowledge of the simulated system exists • Has good performance and offers transparency (compared to similar techniques i.e. Artificial Neural Networks) Figure 1. Planar view of the setup Laser Doppler velocimetry (LDV) measurements of instantaneous stream-wise velocities obtained above a spherical particle at a rate of 450 Hz are treated as model inputs (Figure 1). A camcoder (Figure 2), captures the time instances when the particle is displaced to create the time series of the dislodgement events (considered as model outputs). • Computationally demanding in time and resources, especially when systems of higher dimensions are modeled • Data must be representative of the solution space and obtained accuracy will depend on quality of utilized data. In our case the much lower sampling rate of output data limits predictability The above time series are interpolated to the same time step of 1 ms and divided into training and validation subsets. ANFIS models are "trained" to simulate the dynamics of our physical system utilizing the input-output data subsets, so that the predicted output is close to the observed output (Figure 3). Figure 2. Side view of the setup • Objectives • Limitations of previous techniques • Improve data acquisition techniques (utilize He-Ne laser for accurate displacement measurements) • Include more input vectors (e.g. the vertical velocity component) • Expand developed model and combine it with statistical methods for use in decision making and environmental planning related to stream restoration issues • Identify the dynamics responsible for the entrainment of sediment and other contaminants in natural stream flows • Develop a generic model for predicting the incipient motion of sediment-pollutant particles utilizing Neuro-Fuzzy methods Figure 3. General ANFIS architecture for rule extraction • Currently available sediment entrainment criteria lack generality • Boundary stress averaging methods (first introduced by Shields in 1936) for determining incipient conditions, cannot capture the dynamic characteristics of sediment-flow interactionsdriven by the highly fluctuating nature of turbulence • The accuracy of the models improves with increasing complexity of their structure (Figure 4) • A trade-off exists between interpretability and complexity for rule extraction • The results of ANFIS models and Artificial Neural Network (ANN) architectures are comparable (Figure 4) Visit us: http://www.hydraulicslab.cee.vt.edu/ Figure 4. Error values for the trained ANN and ANFIS structures