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Abstract Background Nutrition health has become a major public health issue in both high and middle-income countries. Nutrition literacy is an important indicator to evaluate the effect of public health intervention and one of the important concepts in health promotion. Thus, this study aimed to verify the reliability and validity of a nutrition literacy assessment questionnaire (NLAQ) and investigate the associated factors of nutrition literacy among college students. Methods We conducted a cross-sectional online survey of college students from April to November 2022 in Wuhan (N = 774). We employed the Cronbach’s alpha coefficient, exploratory and confirmatory factor analysis to evaluate the reliability and validity. We used latent profile analysis to classify the nutrition literacy. We conducted Chi-square test and binary logistic regression to identify the influencing factors. Results The Cronbach’s alpha coefficient of the NLAQ and its dimension was ranging from 0.837 to 0.909. The common factors were consistent with the original dimensions. All indicators met the requirements (χ2/df = 6.16

The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics. Plain Language Summary: The formation of floods is often dynamically influenced by multiple driving factors. Traditional methods based on statistics or hydrological models struggle to clearly understand flood mechanisms due to their limitations. Although deep learning has become an effective tool for flood modeling, its black‐box nature makes it difficult to enrich understanding of flood processes through it. Our proposed interpretable deep learning offers a perspective to unveil the dynamic drivers of floods by extracting patterns from deep learning in a reverse deduction approach. We started with models that perform best on flood extremes and identified the dominant factors and variations of floods across 482 watersheds in the United States. These variations exhibit regional characteristics, with precipitation playing a more significant role and showing more pronounced trends in watersheds where floods are primarily driven by precipitation. We found that variables from 2 days before a flood could have a greater impact than those from the previous days. Furthermore, we revealed the combined impact of variables on flooding through deep learning, showcasing their dynamic changes. Interpretable deep learning explores a new avenue to derive new hydrological insights from inverse data, helping hydrologists better understand natural physical processes. Key Points: Proposed a framework for interpretable time series deep learning based on optimal performance to reveal the dynamic drivers of floodsUnveiled the changes in the dominant driving factors of floods and the roles of driving factors at different moments before a floodElucidated the dynamic responses of floods to the interactions among driving factors [ABSTRACT FROM AUTHOR]

Due to the influence of water and sediment conditions, engineering projects, channel erosion and siltation, river-related factors, and human activities (such as adjustments in floodplain production structures and village construction), there have been significant variations in the transverse gradient of the floodplain in the lower Yellow River. An irrational transverse gradient can lead to the rapid conversion of gravitational potential energy into kinetic energy during the flood evolution process, resulting in increased flow velocity and inundated areas. Exploring reasonable transverse gradients can provide technical support for floodplain management. Using "flood risk assessment" as a keyword, research papers from the Web of Science core database and CNKI published in the past five years were collected. Through a VOS viewer analysis of indicators, a flood risk assessment model based on the "Source–Path–Receptor–Consequence–Resilience" framework was established. A two-dimensional water and sediment model was used to simulate flood inundation scenarios with different transverse gradients in the same flood event, evaluate flood risks in the floodplain, and determine the optimal transverse gradient based on flood risk levels. The results indicate that, compared to low transverse gradients, moderate and high transverse gradients have a more significant driving effect on flood inundation, increasing flood risk opportunities for floodplains. Lower transverse gradients (i.e., TG = 10LG = 1.25‰) are the most favorable for flood protection in the floodplain after flood inundation. [ABSTRACT FROM AUTHOR]

The lower course of the Yellow River is a "hanging river" across the hinterland of China, and the safety of its flood control measures/systems is closely tied to the stability of the nation. Ensuring high-quality, sustainable development of the lower Yellow River floodplain while maintaining flood safety is crucial for the entire Yellow River Basin. Previous studies have primarily focused on the overall development of the Yellow River Basin or the economic–ecological coupling development of cities along the river, often neglecting the flood safety development of the floodplain. This study optimizes the socio-economic–flood-safety–ecological (SFE) system of the typical downstream Landong floodplain within the Yellow River Basin. The system dynamics model (SDM) can simulate the dynamic behavior of SFE systems by constructing mathematical models that incorporate feedback loops and time delays. The primary components include causal loop modules and stock-flow modules. Then, a coupling coordination degree model for the Landong floodplain is established using a comprehensive subjective and objective weighting method, assessing the SFE system's coordination under five scenarios: inertial development, economic development, environmental protection, flood safety, and sustainable development. The results of historical and validity tests indicate that the SDM can effectively simulate the coupling coordination degree of the SFE system. The study results suggest that the coupling coordination degree increases the most under the sustainable development scenario, indicating that the development of the Landong floodplain should not only focus on socio-economic growth, but should also consider flood safety and ecological concerns. In addition, comprehensive regulation from socio-economic, flood safety, and ecological environment indicators are necessary to achieve high-quality, coordinated development. This study has significant implications for policy formulation and management to achieve high-quality and sustainable development in the downstream floodplain of the Yellow River. [ABSTRACT FROM AUTHOR]