Serving as a crucial part of the Yangtze River Basin (YRB)’s flood control system, Flood Detention Areas (FDAs) are vital in mitigating large-scale floods. Urbanization has led to the development of urban FDAs, but significant losses could ensue if these FDAs are activated. With improved reservoirs and embankments, flood pressure in the middle reaches has lessened, posing challenges in balancing flood control and economic benefits. This paper presents a comparative analysis of land use, GDP, and population in FDAs and adjacent cities, enhancing our understanding of their disparities and interrelations. Using the Analytic Hierarchy Process (AHP)–Entropy Weight Method (EW)–Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method, we assess changes in flood control and economic values in FDAs. The results show a conflict between flood control and economic policies in FDAs, highlighting their underestimated economic potential, especially in urban areas. This study identifies differences in economic development across FDAs and a strong correlation between flood control value and inundation rates. Based on evaluations and simulations of the 1954 flood, we provide recommendations for the FDAs’ construction plan, which serves the development and flood management of the YRB and offer insights for similar assessments elsewhere.

As global urbanization accelerates and economic development progresses rapidly, a series of ecological and environmental challenges have emerged. In certain countries, particularly in developing nations such as China, India, and Bangladesh, flood detention areas (FDAs) have been increasingly encroached upon by urbanization, resulting in growing conflicts between flood control functions and economic development. Therefore, accurately predicting urban expansion trends in these regions is considered essential for providing scientific guidance for sustainable regional development. In this study, the PLUS model was selected as the baseline based on comparative experiments. On this foundation, a novel AIF-HOM-PLUS framework was developed. In this framework, a new method, Adjacent Image Fusion (AIF), was proposed to reduce local temporal noise by utilizing adjacent multi-temporal data. Subsequently, Higher-Order Markov chains (HOM) were incorporated to capture complex temporal dependencies and long-term transition patterns. The Middle-Reach Yangtze River urban agglomeration (MRYRUA), including FDAs in the Yangtze River Basin (YRB), was selected as the study area, and LULCCs in 2035 and 2050 were predicted. The results showed the following: (1) among the basic models, the PLUS model exhibited the best performance, while the AIF method significantly improved its overall accuracy (OA) by 2%; (2) the area of impervious surfaces within the FDAs of the YRB will increase at an average annual rate of 1.29%, which pertains to the conflict between the United Nations Sustainable Development Goals (SDGs) 9.1 and SDG 11.a, which has become a critical issue that needs urgent attention; (3) the area of impervious surfaces in the MRYRUA will increase at an average annual rate of 1.3%, primarily at the expense of cropland and water bodies.