Predicting runoff with precision holds immense importance for flood control, water resource management, and basin ecological dispatch. Deep learning, especially long short-term memory (LSTM) neural networks, has excelled in runoff prediction, often outperforming traditional hydrological models. Recent studies suggest that deep learning models employing the self-attention mechanism, such as Transformer and Informer, can achieve even better results than LSTM. However, research exploring the multi-step runoff prediction capabilities of these novel models across diverse scenarios remains scarce. In this investigation, we introduce a relative location coding-enhanced Informer model, termed Rel-Informer, and compare its performance in rainfall-runoff prediction against the standard Informer, Transformer, and LSTM models. The publicly available CAMELS dataset is utilized for training and validating the models, and four experiments are designed: (1) Individual rainfall-runoff modeling (one model per catchment); (2) Regional rainfall-runoff modeling (one model per region); (3) Fine-tuned regional rainfall-runoff modeling (fine-tuned from Experiment 2); (4) Large-scale rainfall-runoff modeling for ungauged catchments (one model for all catchments). The findings reveal that Rel-Informer consistently performs better than the other models, particularly in short-term runoff predictions (1–3 days ahead). Although regional modeling is less precise than individual modeling, it significantly benefits from fine-tuning. The large-scale regional Rel-Informer model effectively predicts runoff in ungauged catchments, showcasing its potential for widespread runoff prediction. This study underscores the influence of hydrological characteristics, such as snowmelt and baseflow indices, on prediction accuracy. In conclusion, the Rel-Informer model, enhanced with improved relative position encoding, emerges as a promising tool for runoff forecasting, especially in data-rich catchments.

To improve the precision of water quality forecasting, the variational mode decomposition (VMD) method was used to denoise the total nitrogen (TN) and total phosphorus (TP) time series and obtained several high- and low-frequency components at four online surface water quality monitoring stations in Poyang Lake. For each of the aforementioned high-frequency components, a long short-term memory (LSTM) network was introduced to achieve excellent prediction results. Meanwhile, a novel metaheuristic optimization algorithm, called the chaos sparrow search algorithm (CSSA), was implemented to compute the optimal hyperparameters for the LSTM model. For each low-frequency component with periodic changes, the multiple linear regression model (MLR) was adopted for rapid and effective prediction. Finally, a novel combined water quality prediction model based on VMD-CSSA-LSTM-MLR (VCLM) was proposed and compared with nine prediction models. Results indicated that (1), for the three standalone models, LSTM performed best in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE), as well as the Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency (KGE). (2) Compared with the standalone model, the decomposition and prediction of TN and TP into relatively stable sub-sequences can evidently improve the performance of the model. (3) Compared with CEEMDAN, VMD can extract the multiscale period and nonlinear information of the time series better. The experimental results proved that the averages of MAE, MAPE, RMSE, NSE, and KGE predicted by the VCLM model for TN are 0.1272, 8.09%, 0.1541, 0.9194, and 0.8862, respectively; those predicted by the VCLM model for TP are 0.0048, 10.83%, 0.0062, 0.9238, and 0.8914, respectively. The comprehensive performance of the model shows that the proposed hybrid VCLM model can be recommended as a promising model for online water quality prediction and comprehensive water environment management in lake systems.

The development of sequence decomposition techniques in recent years has facilitated the wide use of decomposition-based prediction models in hydrological forecasting. However, decomposition-based prediction models usually use the overall decomposition (OD) sampling technique to extract samples. Some studies have shown that the OD sampling technique causes abnormally “high” performance of models owing to the utilization of future information, and this technique cannot be applied in practice. Several researchers have also proposed novel sampling techniques, such as semi-stepwise decomposition (SSD), fully stepwise decomposition (FSD), and single-model SSD (SMSSD). Moreover, an improved single-model FSD (SMFSD) sampling technique is proposed in this study. Four decomposition methods, namely discrete wavelet transform (DWT), empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD), are introduced in this study. A systematic investigation of the models developed using OD sampling techniques is conducted, and the applicability of SSD, FSD, SMSSD, and SMFSD sampling techniques is reasonably evaluated. The application of monthly runoff prediction using the five sampling techniques and four decomposition methods at five representative hydrological stations in Poyang Lake, China, shows that (1) EMD and CEEMDAN (including the improved EMD-based adaptive decomposition method) cannot be used to construct stepwise decomposition prediction models because the implementation of the stepwise decomposition strategy leads to a variable number of sub-series. (2) OD sampling techniques cannot develop convincing models for practical prediction because future information is introduced into the samples for model training. (3) Models developed based on SSD and SMSSD sampling techniques do not use future information in the training process, but suffer from severe overfitting and inferior prediction performance. (4) Models developed based on FSD and SMFSD sampling techniques can produce convincing prediction results, and the combination of the proposed SMFSD sampling technique and VMD develops prediction models with superior performance and significantly enhances the efficiency of the models.

Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1–3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R² = 0.782–0.829, RMSE = 3.240–3.685 MJ m⁻² d⁻¹, MAE = 2.465–2.799 MJ m⁻² d⁻¹, and NRMSE = 0.152–0.173.

Reference Crop evapotranspiration (ET₀) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET₀ (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET₀ is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET₀ of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET₀, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

Thermal unit commitment (UC) is a nonlinear combinatorial optimization problem that minimizes total operating costs while considering system load balance, on/off restrictions and other constraints. Successfully solving the thermal UC problem contributes to a more reliable power system and reduces thermal costs. This paper presents an exact mixed-integer quadratic programming (EMIQP) method for large-scale thermal UC problems. EMIQP revolutionizes the landscape by seamlessly translating the intricate nonlinear combinatorial optimization problem of UC into an exact mixed-integer quadratic formulation. This approach also elegantly reimagines on/off constraints as mixed-integer linear equations, employing both the sum and respective approaches. Our case studies unequivocally demonstrate the exceptional prowess of the EMIQP method, consistently securing the global optimum. Moreover, the mathematical-based EMIQP method produces identical results at each run, which is extremely important for UC in the real world.