Identification of water quality management policy of watershed system with multiple uncertain interactions using a multi-level-factorial risk-inference-based possibilistic-probabilistic programming approach

Abstract

In this study, a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method is proposed for supporting water quality management under multiple uncertainties. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions, and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management in the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function (θ) as well as a higher probability of violating environmental constraints (q ) would correspond to a higher system benefit with an increased risk of violating system feasibility. Chemical plants are the major contributors to biological oxygen demand (BOD) and total phosphorus (TP) discharges; total nitrogen (TN) would be mainly discharged by crop farming. It is also discovered that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of q and benefit of water supply. The findings can help enhance the model's applicability and identify a suitable water quality management policy for environmental sustainability according to the practical situations.

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

“…On a global scale, water quality can be assessed using measures, such as the proportion of the population with sustainable access to an improved water source [190]. However, for a particular water source, water quality typically involves the measurement of components, such as total phosphorus and total nitrogen [129], biological oxygen demand [33,42,129], chemical oxygen demand [132,139,141], dissolved oxygen [139] and Escherichia coli [33]. The impacts of pollution on water sources have been modelled by tools, such as the Streeter-Phelps equation [121] and the Soil and Water Assessment Tool (SWAT) [63].…”

“…Marinoni et al [140] propose a framework for planning major investment decisions and apply this to the case of a water quality enhancement program in a river catchment in Brisbane, Australia. Compromise programming is first used to score the options [42,87,118,121,126,132,139,150,174,176,208,213,224] Storm water [17] Wastewater [1,33,65,80,86,98,106,113,129,139,141,169,187,197,206,219,238] Water allocation [172,230,233] Water trading [224] Water treatment [33,206] Wetlands [39,207,225] for pollution reduction at various sites and the optimal investment problem is then formulated as a multicriteria knapsack problem. In some cases, legislation needs to be considered alongside other management strategies.…”

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

“…Although SIFCP-WRMS has its effectiveness in solving dual uncertainties presented as intervalfuzzy parameters or interval-scenarios, and the results can provide useful strategies for planning the WRMS under the conflict of economic objective, water demand, and sewage discharge, in this study, only one water source (i.e., water from the Water Diversion Project) was considered, and the other water sources, such as surface water, groundwater, and reclaimed water would be further considered to improve the applicability of the SIFCP-WRMS model [43,44]. Moreover, the flow of natural surface water is affected by a variety of factors from climate, topographic, and other aspects, thus, the stochastic programming should be integrated into the SIFCP-WRMS model [45][46][47][48]. Figure 10.…”

A General Amalgamate Technique to Evaluate Human Health Risk Under Uncertain Circumstances

“…Therefore, probability distributions and fuzzy sets with their application are implemented jointly in risk assessment. Some of the related studies are as follows: Guyonnet et al (1999, 2003) discussed hybrid uncertainty modelling; Kentel and Aral (2004) studied the probabilistic-fuzzy health risk modelling; Baudrit et al (2006, 2008) studied probability-fuzzy-based uncertainty modelling; Anoop et al (2008) studied the safety assessment of austenitic steel nuclear power plant pipelines against stress corrosion cracking in the presence of hybrid uncertainties; Baraldi and Zio (2008) combined Monte Carlo and possibilistic approach to uncertainty propagation in event tree analysis; Limbourg and de Rocquigny (2010) studied uncertainty analysis using evidence theory—confronting level 1and level 2 approaches with data availability and computational constraints; Chen et al (2010) proposed a hybrid fuzzy-stochastic modelling approach in environmental risk assessment of offshore produced water discharges; Flage et al (2013) studied the probabilistic and possibilistic treatment of epistemic uncertainties; Karami et al (2013) studied the fuzzy logic and adaptive neuro-fuzzy inference system for characterisation of contaminant exposure; Pedroni et al (2012, 2013) studied the propagation of aleatory and epistemic uncertainties; Arunraj et al (2013) proposed an integrated approach with fuzzy set theory and Monte Carlo simulation for uncertainty modelling in risk assessment; Pastoor et al (2014) studied the road map for human health risk assessment in the twenty-first century; Santillana Farakos et al (2013, 2014, 2016) studied the risk assessment for Salmonella in tree nuts, Salmonella in low-water activity foods and Salmonella in low-moisture foods; Zwietering (2015) studied the uncertainty modelling for risk assessment and risk management for safe foods; Rębiasz et al (2017) studied the joint treatment of imprecision and randomness in the appraisal of the effectiveness and risk of investment projects; Innal et al (2016) studied the uncertainty handling in safety instrumented systems according to International Electrotechnical Commission (IEC) and the new proposal based on coupling Monte Carlo analysis and fuzzy sets; Abdo and Flaus (2016) proposed a new approach with randomness and fuzzy theory for uncertainty quantification in dynamic system risk assessment; Zhang et al (2016) studied the risk assessment of shallow groundwater contamination under irrigation and fertilisation conditions; Dutta (2017) devised a technique for joint propagation of aleatory and epistemic uncertainties; Liu et al (2017) studied the pos...…”