**3. Uncertainty management in risk assessments**

Uncertainty management is used to build confidence in the outcome of the RA results. Subsequently, the adaptation of well-developed uncertainty identification, classification, inventory, quantification and assessment, and combination schemes are essential for reliable decision making process. In the first step, identification of inherent uncertainty sources in the studied system is achieved. In the uncertainty combination step, the total uncertainty is obtained by aggregating all the quantified uncertainties, where different forms of uncertainties with different mathematical presentations are aggregated to produce a confidence sentence in the system performance. In this section, approaches to classify, inventory and quantify uncertainties will be introduced.

### **3.1 Uncertainty classification**

In general, different sources of uncertainty associate the problem identification and risk analysis phases in the risk assessment methodology. These uncertainties might be related to the system variability and randomness, the presence of errors, either in the measurements, or modeling and analysis, scenarios or data insignificance, and lack of knowledge, indeterminacy, judgment, and linguistic imprecision in decision making [17–25]. Some of these uncertainties could be reduced and others are irreducible.

Two uncertainty classification systems are used; the first is based on the ability to reduce these uncertainties and the second is based on their sources [18–24]. The first consists of two classes, i.e. Epistemic & Aleatory, and this system is effectively used in building confidence in the uncertainty management outcomes, where:


It should be noted that during uncertainty management it is important to differentiate between these types and justify the consideration of certain type that associates the features, events, or processes (FEP) of the studied system towards reliable uncertainty quantification.

Uncertainty classification based on the uncertainty sources includes the following classes, where each class includes both epistemic and aleatory uncertainties [23, 24]:


It should be noted that both types of uncertainty classifications are used to quantify, assess and minimize the uncertainty in the decision making process. Skinner et al. developed a classification system based on the ability of the uncertainty to be reduced and their location, in the system, data, model and the subjective uncertainty in the form or language, extrapolation and decision, and their associated sub-location as illustrated in **Figure 2** [19].
