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The accurate prediction of monthly runoff in the lower reaches of the Yellow River is crucial for the rational utilization of regional water resources, optimal allocation, and flood prevention. This study proposes a VMD-SSA-BiLSTM coupled model for monthly runoff volume prediction, which combines the advantages of Variational Modal Decomposition (VMD) for signal decomposition and preprocessing, Sparrow Search Algorithm (SSA) for BiLSTM model parameter optimization, and Bi-directional Long and Short-Term Memory Neural Network (BiLSTM) for exploiting the bi-directional linkage and advanced characteristics of the runoff process. The proposed model was applied to predict monthly runoff at GaoCun hydrological station in the lower Yellow River. The results demonstrate that the VMD-SSA-BiLSTM model outperforms both the BiLSTM model and the VMD-BiLSTM model in terms of prediction accuracy during both the training and validation periods. The Root-mean-square deviation of VMD-SSA-BiLSTM model is 30.6601, which is 242.5124 and 39.9835 lower compared to the BiLSTM model and the VMD-BiLSTM model respectively; the mean absolute percentage error is 5.6832%, which is 35.5937% and 6.3856% lower compared to the other two models, respectively; the mean absolute error was 19.8992, which decreased by 136.7288 and 25.7274 respectively; the square of the correlation coefficient ( R 2 ) is 0.93775, which increases by 0.53059 and 0.14739 respectively; the Nash–Sutcliffe efficiency coefficient was 0.9886, which increased by 0.4994 and 0.1122 respectively. In conclusion, the proposed VMD-SSA-BiLSTM model, utilizing the sparrow search algorithm and bidirectional long and short-term memory neural network, enhances the smoothness of the monthly runoff series and improves the accuracy of point predictions. This model holds promise for the effective prediction of monthly runoff in the lower Yellow River.

Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R 2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R 2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

To improve the accuracy of runoff forecasting, a combined forecasting model is established by using the kernel extreme learning machine (KELM) algorithm optimised by the butterfly optimisation algorithm (BOA), combined with the variational modal decomposition method (VMD) and the complementary ensemble empirical modal decomposition method (CEEMD), for the measured daily runoff sequences at Jiehetan and Huayuankou stations and Gaochun and Lijin stations. The results show that the combined model VMD-CEEMD-BOA-KELM predicts the best. The average absolute errors are 30.02, 23.72, 25.75, 29.37, and the root mean square errors are 20.53 m 3 /s, 18.79 m 3 /s, 18.66 m 3 /s, and 21.87 m 3 /s, the decision coefficients are all above 90 percent, respectively, and the Nash efficiency coefficients are all more than 90%, from the above it can be seen that the method has better results in runoff time series prediction.

In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as \"Very good,\" 2 as \"Good,\" 2 as \"Satisfactory,\" and 1 as \"Unsatisfactory\" among the 14 regions. The model achieved an NSE of 0.86, R 2 of 0.85, and PBIAS of −2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making.

Hebi is located in the northern part of China’s Henan Province and is a typical receiving area for China’s South-to-North Water Diversion Project. The assessment of habitat quality and water yield over a long time series is important for evaluating the stability of ecosystem services in Hebi and other receiving areas and for maintaining ecological security and promoting sustainable development. This paper aims to evaluate and dynamically analyse habitat quality and water yield in Hebi, and analyses the characteristics of changes in spatial and temporal patterns of land cover types, habitat quality and water yield in Hebi over the past 20 years, using 2000, 2005, 2010, 2015 and 2020 as horizontal years. The results indicate that: (1) During the study period, the overall land use type in Hebi City has been constantly changing, with the most significant conversion from arable land to other land types; combined with its landscape pattern index, Hebi City has a general characteristic of significant landscape fragmentation and complexity in land use. (2) Habitat quality in Hebi shows an overall trend towards better development, with water availability decreasing and then increasing; the zoning of ecosystem services in Hebi is divided into three classes: superior, good and general, with the area covered by the superior and general classes expanding year by year. (3) Correlation analysis by SPSS software shows that the correlation between habitat quality and landscape pattern index is greater than the correlation between habitat quality and climate change. Additionally, the correlation between water availability and climate change is greater than the correlation between water availability and landscape pattern index.

Enhancing flood forecasting accuracy, promoting rational water resource utilization and management, and mitigating river disasters all hinge on the crucial role of improving the accuracy of daily flow prediction. The coupled model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Bidirectional Long Short-Term Memory (BiLSTM) demonstrates higher stability when faced with nonlinear and non-stationary data, stronger adaptability to various types and lengths of time series data by utilizing sample entropy, and significant advantages in processing sequential data through the BiLSTM network. In this study, in the context of predicting daily flow at the Huayuankou Hydrological Station in the lower reaches of the Yellow River, a coupled CEEMDAN–SE–BiLSTM model was developed and utilized. The results showed that the CEEMDAN–SE–BiLSTM coupled model achieved the utmost accuracy in prediction and optimal fitting performance. Compared with the CEEMDAN–SE–LSTM, CEEMDAN–BiLSTM, and BiLSTM coupled models, the root mean square error (RMSE) of this model is reduced by 42.77, 182.02, and 193.71, respectively; the mean absolute error (MAE) is reduced by 37.62, 118.60, and 126.67, respectively; and the coefficient of determination (R 2 ) is increased by 0.0208, 0.1265, 0.1381.

Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation.

Despite growing evidence of primary care-based interventions for chronic disease management in resource-limited settings, long-term post-trial effects remain inconclusive. We investigated the association of a 12-month system-integrated technology-enabled model of care (SINEMA) intervention with mortality outcomes among patients experiencing stroke at 6-year post-trial. This study (clinicltiral.gov registration number: NCT05792618) is a long-term passive observational follow-up of participants and their spouse of the SINEMA trial (clinicaltrial.gov registration number: NCT03185858). The original SINEMA trial was a cluster-randomized controlled trial conducted in 50 villages (clusters) in rural China among patients experiencing stroke during July 2017-July 2018. Village doctors in the intervention arm received training, incentives, and a customized mobile health application supporting monthly follow-ups to participants who also received daily free automated voice-messages. Vital status and causes of death were ascertained using local death registry, standardized village doctor records, and verbal autopsy. The post-trial observational follow-up spanned from 13- to 70-months post-baseline (up to April 30, 2023), during which no intervention was requested or supported. The primary outcome of this study was all-cause mortality, with cardiovascular and stroke cause-specific mortality also reported. Cox proportional hazards models with cluster-robust standard errors were used to compute hazard ratios (HRs) and 95% confidence intervals (95% CIs), adjusting for town, age, and sex in the main analysis model. Analyses were conducted on an intention-to-treat basis. Of 1,299 patients experiencing stroke (mean age 65.7 years, 42.6% females) followed-up to 6 years, 276 (21.2%) died (median time-to-death 43.0 months [quantile 1-quantile 3: 26.7-56.8]). Cumulative incidence of all-cause mortality was 19.0% (121 among 637) in the intervention arm versus 23.4% (155 among 662) in the control arm (HR 0.73; 95% CI 0.59, 0.90; p = 0.004); 14.4% versus 17.7% (HR 0.73; 95% CI 0.58, 0.94; p = 0.013) for cardiovascular cause-specific mortality; and 6.0% versus 7.9% (HR 0.71; 95% CI 0.44, 1.15; p = 0.16) for stroke cause-specific mortality. Although multisource verification was used to verify the outcomes, limitations exist as the survey- and record-matching-based nature of the study, unavailability of accurate clinical diagnostic records for some cases and the potential confounders that may influence the observed association on mortality. Despite no observed statistically difference on stroke cause-specific mortality, the 12-month SINEMA intervention, compared with usual care, significantly associated with reduced all-cause and cardiovascular cause-specific mortality during 6 years of follow-up, suggesting potential sustained long-term benefits to patients experiencing stroke.

This paper analyzes the seismic performance and damage characteristics of high-rise frame-core-tube structures on soft soil, explicitly incorporating dynamic soil–pile–structure interaction (SSI). A refined 3D finite element model of a 52-storey soil–pile–structure system was developed in ABAQUS, utilizing viscous-spring boundaries and the equivalent nodal force method for seismic input. Nonlinear analyses under six seismic waves were compared to a fixed-base model neglecting SSI. Key findings demonstrate that SSI significantly alters structural response; it amplifies lateral displacements and inter-storey drift ratios throughout the structure, particularly at the top level. While total base shear decreased, frame column base shear forces substantially increased. SSI also reduced peak top-storey accelerations, diminished short-period spectral components, and prolonged the predominant period of response spectra. Analysis of member damage revealed SSI generally reduced compressive and tensile damage in core walls, floor slabs, and frame beams. Principal compressive stresses at the base of frame columns increased under SSI. These results highlight the necessity of including dynamic SSI in seismic analysis for high-rises on soft soil, specifically due to its detrimental amplification of forces in frame columns.

In low- and middle-income countries, fewer than 1 in 10 people with mental health conditions are estimated to be accurately diagnosed in primary care. This is despite more than 90 countries providing mental health training for primary healthcare workers in the past two decades. The lack of accurate diagnoses is a major bottleneck to reducing the global mental health treatment gap. In this commentary, we argue that current research practices are insufficient to generate the evidence needed to improve diagnostic accuracy. Research studies commonly determine accurate diagnosis by relying on self-report tools such as the Patient Health Questionnaire-9. This is problematic because self-report tools often overestimate prevalence, primarily due to their high rates of false positives. Moreover, nearly all studies on detection focus solely on depression, not taking into account the spectrum of conditions on which primary healthcare workers are being trained. Single condition self-report tools fail to discriminate among different types of mental health conditions, leading to a heterogeneous group of conditions masked under a single scale. As an alternative path forward, we propose improving research on diagnostic accuracy to better evaluate the reach of mental health service delivery in primary care. We recommend evaluating multiple conditions, statistically adjusting prevalence estimates generated from self-report tools, and consistently using structured clinical interviews as a gold standard. We propose clinically meaningful detection as ‘good-enough’ diagnoses incorporating multiple conditions accounting for context, health system and types of interventions available. Clinically meaningful identification can be operationalized differently across settings based on what level of diagnostic specificity is needed to select from available treatments. Rethinking research strategies to evaluate accuracy of diagnosis is vital to improve training, supervision and delivery of mental health services around the world.