Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to the supervised learning category to evaluate the performance of DL models in terms of streamflow forecasting. They include a feed-forward neural network (FFNN), a convolutional neural network (CNN), and four LSTM-based models. Two standard models with just one hidden layer-LSTM and gated recurrent unit (GRU)-are used against two more complex models-the stacked LSTM (StackedLSTM) model and the Bidirectional LSTM (BiLSTM) model. The Red River basin-the largest river basin in the north of Vietnam-was adopted as a case study because of its geographic relevance since Hanoi city-the capital of Vietnam-is located downstream of the Red River. Besides, the input data of these models are the observed data at seven hydrological stations on the three main river branches of the Red River system. This study indicates that the four LSTM-based models exhibited considerably better performance and maintained stability than the FFNN and CNN models. However, the complexity of the StackedLSTM and BiLSTM models is not accompanied by performance improvement because the results of the comparison illustrate that their respective performance is not higher than the two standard models-LSTM and GRU. The findings of this study present that LSTM-based models can reach impressive forecasts even in the presence of upstream dams and reservoirs. For the streamflow-forecasting problem, the LSTM and GRU models with a simple architecture (one hidden layer) are sufficient to produce highly reliable forecasts while minimizing the computation time.

The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

The present study aimed to determine thiamine intake levels and the association between thiamine intake, diabetes, cardiovascular diseases and mental health. Participants were interviewed to obtain data on socio-demographic characteristics, lifestyle, current medications, medical and family history. The daily intake of thiamine was assessed by a 24-h recall. The mean age of the 34 700 study subjects was 42⋅9 years (sd 22⋅8, min–max: 1–80) and 19 342 (55⋅7 %) were women. The levels of thiamine intake were 1⋅126 mg (2016), 1⋅115 mg (2017) and 1⋅087 mg (2018) for women, which were equal to or only slightly above the recommended intake of 1⋅10 mg/d for women. The levels of thiamine intake from 2014–15 and 2016–18 significantly decreased. The estimated percentage of insufficient thiamine intake was 37⋅8 % (95 % CI 37⋅3, 38⋅4). Multivariable regression analysis adjusted for potential confounders showed that thiamine intake was critically associated with lower risks of hypertension, MI or angina, type 2 diabetes, depression and dyslipidemia. The daily thiamine intake from food can reversal the risks of hypertension (OR 0⋅95; 95 % CI 0⋅90, 0⋅99), MI or angina (OR 0⋅84; 95 % CI 0⋅74, 0⋅95), type 2 diabetes (OR 0⋅86; 95 % CI 0⋅81, 0⋅93), depression (OR 0⋅90; 95 % CI 0⋅83, 0⋅97) and dyslipidemia (OR 0⋅90; 95 % CI 0⋅86, 0⋅95), respectively. Further works are needed to identify the effects of thiamine and non-communicable diseases (NCDs) and mental health. A preventive thiamine supplementation strategy should be adopted to target NCDs and mental health and risk factors associated with thiamine deficiency. The optimisation of NCD control and mental health protection is also a vital integral part of Korea's public health system.