Reliability-Oriented MultiObjective Optimal Decision Making Approach for Uncertainty Based Watershed Load Reduction

Feifei Dong, Yong Liu, Han Su, Rui Zou, Huaicheng Guo

Science of the Total Environment

ABSTRACT: Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore,optimal decision-making modeling in watershed load reduction is suffering due to the followingchallenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decisionschemes may be vulnerable to failure.The probability that solutions are feasible under uncertainties is defined asreliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. Thisstudy aimed to maximize reliability levels from considerations of cost and load reductions.The Pareto solutions of the ROMO optimizationmodelwere generated with the multi-objective evolutionary algorithm, demonstrating schemes representingdifferent biases toward reliability.The Pareto fronts of six maximum allowable emissions (MAE) scenarios were obtained,which indicated that decisions may be unreliable under unpractical loadreduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide ashortcut for identifying schemes at specific reliability levels for decisionmakers. The model results indicated thatthe ROMO approach canoffer decision makers great insights into reliabilitytradeoffs and can thushelp them to avoid ineffective decisions.

KEYWORDS: Stochastic; Multi-objectiveEvolutionary Algorithm; Pareto Fronts;Tradeoff Analysis; Back Propagation Neural Network