Improved variable scale optimisation algorithm in water saving landscape design and its application

With the acceleration of urbanisation, the problem of water resource shortage is becoming increasingly serious. Currently, the water use situation in Chinese cities is severe and the contradiction between the supply and demand of water resources is prominent, which has become one of the main factors restricting sustainable development of cities. Traditional research on water-saving landscape design focuses mainly on the analysis of the efficiency of water resource utilisation within a single area or city. The scope of the research is limited and the research methods are individual, which cannot fully meet the current needs of sustainable urban water-saving landscape design. The improved variable scale optimisation algorithm has become an indispensable tool in modern landscape design and would play a more important role in landscape design and ecological environment protection in the future. In response to the shortcomings of traditional variable-scale optimisation algorithms in solving the optimisation problem of water-saving landscape design for ecological sustainable development, this article would use an improved variable-scale optimisation algorithm to study the water-saving landscape design for ecological sustainable development in Community X, City B, Province A. The research results indicated that there were three experts who rated the effect of the water saving landscape design of the first group of designers as good or above, accounting for 37.5%. The number of people who rated the effectiveness of water-saving landscape design in the second group as good or higher was 8, representing 100%. The improved variable-scale optimisation algorithm could effectively improve the application of water-saving landscape design in ecological sustainable development.

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).