The cloud model based stochastic multi-criteria decision making technology for river health assessment under multiple uncertainties

The river health assessment (RHA) usually concerns multiple criteria including streamflow, ecology, physical structure, water quality and social service functions and evaluation standards formulated by managers as well as groups with different interests. Conventional multi-criteria decision making (MCDM) models are used to comprehensively evaluate river condition through multiple criteria under deterministic environment. In fact, uncertainties are always existed in these criteria data since they are correlated with hydrology and water resources system. In this work, we proposed a stochastic cloud model based MCDM framework for solving RHA considering multiple uncertainties in criteria performance values (PVs) and criteria weights (CWs). The cloud model is allied to describe uncertainty of PVs using numerous drops following normal distribution generated by forward drop generator (FDG). The minimum deviation principle based aggregated CWs are utilized to efficiently quantify uncertainty in CWs and reduce conflict between multiple CWs sources. A novel stochastic multi-criteria acceptability analysis (SMAA) is developed coupling with grey correlation analysis (GCA) and TOPSIS. The risk information for river evaluation caused by multiple uncertainties are described using quantified decision error risk (QDER) and rank uncertainty degree (RUD). The proposed methodology is verified practicability by applying it to river health evaluation in Taihu basin. The numerical simulations are conducted to demonstrate superiority and efficiency of novel SMAA in comparison with conventional SMAA and deterministic MCDM models based on GCA and TOPSIS. The robustness analysis is implemented to disclose its computation stability and reliability as well as effects of cloud parameters on final MCDM results. The results of novel SMAA show that it provides river managers with comprehensive river health and risk analysis information, assisting them to make highly reliable assessment and adopt effective measures harnessing rivers.