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An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models. Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives. Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty. Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and pre-evaluation. First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information. Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine. The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives. The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.

Water Resource Sustainable Utilization (WRSU) is becoming increasingly important, given growing water resource shortages and widening gaps between water supply and demand. Most existing studies have focused on WRSU levels without a dedicated strategy-oriented framework. In addition, uncertainties occur in the process of indicator quantification and grading, leading to a lack of accuracy in the assessment results. Therefore, in this study, stemming from water resource, societal, economic, and environmental dimensions, an indicator system with qualitative description was introduced by Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to enable development and selection of sustainable water use strategies. A normal cloud model that is capable of addressing uncertainties was used to determine WRSU levels. The comprehensive evaluation results can both reflect the WRSU levels and select the most suitable strategy. The model’s utility was demonstrated by applying it to the case of Shandong province in China. Based on the results, most areas of Shandong province appear to be facing serious unsustainable issues. Appropriate development strategies based on the WRSU levels were provided for improving sustainable use of water resources. The proposed method offers an efficient means for WRSU assessment and strategy development. Moreover, it has the potential to be applied to other water resource issues.

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.