Wendy Huang

A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems

In this study, an uncertainty quantification framework is proposed for hydrologic models based on probabilistic collocation method (PCM). The PCM method first uses polynomial chaos expansion (PCE) to approximate the hydrological outputs in terms of a set of standard Gaussian random variables, and then estimates the unknown coefficients in the PCE through collocation method. The conceptual hydrologic model, Hymod, is used to demonstrate the applicability of PCM in quantifying uncertainties of the hydrologic predictions. Two parameters in Hymod are considered as uniformly distributed in certain intervals. Two-dimensional 2-order and two-dimensional 3-order PCEs are applied to quantify the uncertainty of Hymod’s predictions. The results indicate that, both 2- and 3-order PCEs can well reflect the uncertainty of the streamflow predictions. The means and variances of 2- and 3-order PCEs are consistent with those obtained by Monte Carlo (MC) simulation method. However, for detailed distributions at selected periods, the histograms obtained by 3-order PCE are more accurate than those generated by 2-order PCE, when compared with the histograms obtained by MC simulation method.

Development of a Stepwise-Clustered Hydrological Inference Model

AbstractFlow prediction is one of the most important issues in modern hydrology. In this study, a statistical tool, stepwise-clustered hydrological inference (SCHI) model, was developed for daily streamflow forecasting. The SCHI model uses cluster trees to represent the nonlinear and complex relationships between streamflow and multiple factors related to climate and watershed conditions. It allows a great deal of flexibility in watershed configuration. The proposed model was applied to the daily streamflow forecasting in the Xiangxi River watershed, China. The correlation coefficient for calibration (1991–1995) was 0.881, and that for validation (1996–1998) was 0.771. Nash–Sutcliffe efficiencies for calibration and validation were 0.768 and 0.577, respectively. The results were compared to those of a conventional process-based model, and it was found that the SCHI model had a superior performance. The results indicate that the proposed model could provide not only reliable and efficient daily flow predic...

Probabilistic Inference Coupled with Possibilistic Reasoning for Robust Estimation of Hydrologic Parameters and Piecewise Characterization of Interactive Uncertainties

AbstractThis paper presents a factorial possibilistic–probabilistic inference (FPI) framework for estimation of hydrologic parameters and characterization of interactive uncertainties. FPI is capable of incorporating expert knowledge into the parameter adjustment procedure for enhancing the understanding of the nature of the calibration problem. As a component of the FPI framework, a Monte Carlo–based fractional fuzzy–factorial analysis (MFA) method is also proposed to identify the best parameter set and its underlying probability distributions in a fuzzy probability space. Factorial analysis of variance (ANOVA) coupled with its multivariate extensions are performed to explore potential interactions among model parameters and among hydrological metrics in a systematic manner. The proposed methodology is applied to the Xiangxi River watershed by using the conceptual hydrological model (HYMOD) to demonstrate its validity and applicability. Results reveal that MFA is capable of deriving probability density f...

Insight into sorption mechanism of phenanthrene onto gemini modified palygorskite through a multi-level fuzzy-factorial inference approach

ABSTRACT A multi-level fuzzy-factorial inference approach was proposed to examine the sorption behavior of phenanthrene on palygorskite modified with a gemini surfactant. Fuzzy set theory was used to determine five experimentally controlled environmental factors with triangular membership functions, including initial concentration, added humid acid dose, ionic strength, temperature, and pH. The statistical significance of factors and their interactions affecting the sorption process was revealed through a multi-level factorial experiment. Initial concentration, ionic strength, and pH were identified as the most significant factors based on the multi-way ANOVA results. Examination of curvature effects of factors revealed the nonlinear complexity inherent in the sorption process. The potential interactions among experimental factors were detected, which is meaningful for providing a deep insight into the sorption mechanisms under the influences of factors at different levels.

Critical factors and their effects on product maturity in food waste composting

Product maturity represents the efficiency of composting performance, and it calls for high attention in food waste composting. In this study, a 24-1 fractional factorial design method combined with well-controlled experiments was introduced to characterize the effects of system factors (i.e., C/N ratio, aeration rate, starting culture amount, and coal ash amendment) on product maturity of food waste composting. The compost maturity was synthetically evaluated by developing a Mamdani fuzzy rule-based inference system. Temperature index, O2 uptake rate, ammonium, OM loss, C/N ratio, and ash content were chosen as indicators of the fuzzy multicriterion maturity evaluation. Evaluation results of compost maturity for the eight experiment runs demonstrated that the proposed method is capable of evaluating the compost maturity in food waste composting. The effect analyses indicated that the starting culture amount and aeration rate contributed the most to the compost maturity in this study. The results could provide decision support for the process control in food waste composting management.

Electrochemical techniques for evaluating short-chain fatty acid utilization by bioanodes

The utilization of propionic, n-butyric, and isobutyric acids in microbial electrolysis cells (MECs) was examined by monitoring individual short-chain fatty acid concentration and using electrochemical techniques, such as linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). When n-butyric or isobutyric acid was provided as a single substrate, acetic acid was consistently observed in experiments, indicating that acetic acid was produced as a byproduct and utilized by exoelectrogenic bacteria as an additional substrate in MECs. When isobutyric acid was given as a sole substrate, the applied potential governed the electric current (i.e., rate of substrate utilization). In addition, the coulombic efficiency was substantially high (90%), indicating direct utilization of isobutyric acid by exoelectrogenic bacteria. However, the coulombic efficiency was relatively low (30–60%) when n-butyric acid was provided as a sole substrate. In another experiment, the magnitude of electric current was more dependent on the concentration of acetic acid than that of other short-chain fatty acids. In the EIS analysis, the exchange current was found to be a more reliable indicator of substrate favorability than the charge transfer resistance.