Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.
In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives.
MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China. Electronic address: zjunlong0801@163.com.
J L
JL
Zhang
Environment and Energy Systems Engineering Research Center, UR-BNU, Beijing Normal University, Beijing 100875, China. Electronic address: yongping.li@iseis.org.
Y P
YP
Li
Environment and Energy Systems Engineering Research Center, UR-BNU, Beijing Normal University, Beijing 100875, China. Electronic address: gordon.huang@uregina.ca.
G H
GH
Huang
Department of Civil Engineering, McMaster University, Hamilton, ON. L8S 4L7, Canada. Electronic address: baetz@mcmaster.ca.
B W
BW
Baetz
MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China. Electronic address: zyljing@126.com.
J
J
Liu
D001499:Bayes Theorem; D003657:Decision Making; D012987:Soil; D035501:Uncertainty; D060753:Water Quality
D012987:Soil
Bayesian estimation; Effluent trading; MCMC; Nutrient transport; Probabilistic–possibilistic optimization; Water quality