Department of Biological Systems Engineering

Permanent URI for this community

https://hdl.handle.net/10919/24227
Biological Systems Engineering (BSE) is the engineering discipline that applies concepts of biology, chemistry and physics, along with engineering science and design principles, to solve problems in biological systems. Our faculty and students work in a broad range of biological systems, from natural systems, such as watersheds with a focus on water resources, to built systems, such as bioreactors and bioprocessing facilities. We work from the nanoscale to the macroscale. We seek to improve animal, human, and environmental health through development and design of healthy food products, vaccines, bioenergy, biomaterials, and water quality management practices. We convert biological resources, such as switchgrass, plant proteins, and animal manure, into value-added products, such as biopharmaceuticals, biofuels, and biomaterials, in a sustainable manner.

Browse

Browsing Department of Biological Systems Engineering by Author "Ahmadisharaf, Ebrahim"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Wolfe, Mary Leigh; Leman, Scotland C.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (2018-12)
    This study presents a probabilistic framework that considers both the water quality improvement capability and reliability of alternative total maximum daily load (TMDL) pollutant allocations. Generalized likelihood uncertainty estimation and Markov chain Monte Carlo techniques were used to assess the relative uncertainty and reliability of two alternative TMDL pollutant allocations that were developed to address a fecal coliform (FC) bacteria impairment in a rural watershed in western Virginia. The allocation alternatives, developed using the Hydrological Simulation Program-FORTRAN, specified differing levels of FC bacteria reduction from different sources. While both allocations met the applicable water-quality criteria, the approved TMDL allocation called for less reduction in the FC source that produced the greatest uncertainty (cattle directly depositing feces in the stream), suggesting that it would be less reliable than the alternative, which called for a greater reduction from that same source. The approach presented in this paper illustrates a method to incorporate uncertainty assessment into TMDL development, thereby enabling stakeholders to engage in more informed decision making.
  • Thumbnail Image
    Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran
    Janizadeh, Saeid; Avand, Mohammadtaghi; Jaafari, Abolfazl; Phong, Tran Van; Bayat, Mahmoud; Ahmadisharaf, Ebrahim; Prakash, Indra; Pham, Binh Thai; Lee, Saro (MDPI, 2019-09-30)
    Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.
  • Thumbnail Image
    Risk-based decision making to evaluate pollutant reduction scenarios
    Ahmadisharaf, Ebrahim; Benham, Brian L. (2020-02)
    A total maximum daily load (TMDL) is required for water bodies in the U.S. that do not meet applicable water quality standards. Computational watershed models are often used to develop TMDL pollutant reduction scenarios. Uncertainty is inherent in the modeling process. An explicit uncertainty analysis would improve model performance and result in more robust decision making when comparing alternative pollutant reduction scenarios. This paper presents a risk-based framework for evaluating alternative pollutant allocation scenarios considering reliability in achieving water quality goals. We demonstrate a generic routine for the application of Generalized Likelihood Uncertainty Estimation (GLUE) to Hydrological Simulation Program-FORTRAN (HSPF) using existing softwares to evaluate two bacteria reduction scenarios from a recently developed TMDL that addressed a bacterial impairment in a mixed land use watershed in Virginia, U.S. Our probabilistic analysis showed that for reliability levels <25%, the recommended TMDL bacterial load reduction scenario had the same exceedance rate as the full reduction scenario (fully reducing all bacterial loads except wildlife), while for reliability levels between 25% and 50%, the exceedance rates for the two pollutant reduction scenarios were similar, with the TMDL recommended scenario violating the water quality criteria only slightly more often. The full reduction scenario performed better in higher reliability levels, although it could not meet the water quality criteria. Our results indicated that, in this case, achieving water quality goals with very high reliability was not possible, even with extreme levels of pollutant reduction. The risk-based framework presented here illustrates a method to propagate watershed model uncertainty and assess performance of alternative pollutant reduction scenarios using existing tools, thereby enabling decision makers to understand the reliability of a given scenario in achieving water quality goals. (C) 2019 The Author(s). Published by Elsevier B.V.
  • Thumbnail Image
    Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed
    Ahmadisharaf, Ebrahim; Kalyanapu, Alfred J.; Chung, Eun-Sung (MDPI, 2017-09-26)
    An integrated framework is presented for sustainability-based flood hazard mapping of the Swannanoa River watershed in the state of North Carolina, U.S. The framework uses a hydrologic model for rainfall–runoff transformation, a two-dimensional unsteady hydraulic model flood simulation and a GIS-based multi-criteria decision-making technique for flood hazard mapping. Economic, social, and environmental flood hazards are taken into account. The importance of each hazard is quantified through a survey to the experts. Utilizing the proposed framework, sustainability-based flood hazard mapping is performed for the 100-year design event. As a result, the overall flood hazard is provided in each geographic location. The sensitivity of the overall hazard with respect to the weights of the three hazard components were also investigated. While the conventional flood management approach is to assess the environmental impacts of mitigation measures after a set of feasible options are selected, the presented framework incorporates the environmental impacts into the analysis concurrently with the economic and social influences. Thereby, it provides a more sustainable perspective of flood management and can greatly help the decision makers to make better-informed decisions by clearly understanding the impacts of flooding on economy, society and environment.
  • Thumbnail Image
    Two-phase Monte Carlo simulation for partitioning the effects of epistemic and aleatory uncertainty in TMDL modeling
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (ASCE, 2018-10-29)
    A two-phase Monte Carlo simulation (TPMCS) uncertainty analysis framework is used to analyze epistemic and aleatory uncertainty associated with simulated exceedances of an in-stream fecal coliform (FC) water quality criterion when using the Hydrological Simulation Program-FORTRAN (HSPF). The TPMCS framework is compared with a single-phase or standard Monte Carlo simulation (SPMCS) analysis. Both techniques are used to assess two total maximum daily load (TMDL) pollutant allocation scenarios. The application of TPMCS illustrates that cattle directly depositing FC in the stream is a greater source of epistemic uncertainty than FC loading from cropland overland runoff, the two sources specifically targeted for reduction in the allocation scenario. This distinction is not possible using SPMCS. Although applying the TPMCS framework involves subjective decisions about how selected model parameters are considered within the framework, this uncertainty analysis approach is transparent and the results provide information that can be used by decision makers when considering pollution control measure implementation alternatives, including quantifying the level of confidence in achieving applicable water quality standards. © American Society of Civil Engineers (ASCE).