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The sustainable and integrated management of resources is a crucial approach to achieving sustainable regional development in the face of limited land and increasingly scarce water resources. This paper proposes a multidimensional optimization model to jointly optimize land use patterns and water resource allocation under the conditions of maximizing economic benefits and minimizing social differences, ecological loads, environmental pollution, and carbon emissions. The optimization model is solved using the affiliation function method and then combined with random forest (RF) and cellular automata (CA) models to further obtain the optimal spatial layout of land use. This paper takes the Mid-Yangtze River City Cluster as an example and optimizes the land use pattern and water allocation in the region in 2020 and 2030. The results of the study show that the optimized water and land resource allocation scheme performs better than the status quo in all dimensions. The optimized economic benefits, social effects, ecological effects, environmental effects, and overall sustainability in 2020 are increased by 45.97%, 22.89%, 10.07%, 24.65%, and 42.73%, respectively. By 2030, overall sustainability reaches 0.69. The developed modeling framework helps to elucidate the internal linkages between land use and water allocation, and it provides decision-makers with diversified approaches to land and water resource management and adjustment for different cities in different periods, which promotes the sustainable development of urban agglomerations. [Display omitted] • The intrinsic relationship between different land use types and water demand is addressed. • A sustainable approach to integrated management of land and water resources is proposed. • The synergistic optimization of the economy, society, ecology and the environment is developed. • The spatial patterns of land use for the balanced development of urban agglomerations are promoted. [ABSTRACT FROM AUTHOR]

Enhancing the sustainability of large-scale infrastructure construction is the foundation and trend of urban sustainable development and environmental protection. This paper proposes a perception and evaluation method based on Dynamic Fuzzy Cognitive Mapping (D-FCM) to address the dynamics, uncertainties, and implementation difficulties of sustainability evaluation in large-scale infrastructure construction. Using Hebbian rules to learn the experience and knowledge of experts, a dynamic evolution correlation matrix about evaluation indicators is obtained, and a D-FCM dynamic system model is established to obtain the sustainability evaluation results of large-scale infrastructure construction. The comprehensive sustainable evaluation value (K) of the selected subway project case is 0.934, which belongs to the V level in the sustainable level classification standard and has a good trend in sustainable development. Social public participation (S1), light pollution prevention (H1), wastewater discharge compliance rate (H2), soil pollution prevention (H3), and soil and water conservation and utilization (Z2) are the most significant and sensitive indicators for the sustainable development of large-scale infrastructure construction. Based on the interaction of influencing factors in the D-FCM model, an economically efficient control measures and decision-making plan can be proposed to improve the sustainability of large-scale infrastructure construction projects. [ABSTRACT FROM AUTHOR]