Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

Background: The pathogenesis of neuropathic pain and the reasons for the prolonged unhealing remain unknown. Increasing evidence suggests that sex oestrogen differences play a role in pain sensitivity, but few studies have focused on the oestrogen receptor which may be an important molecular component contributing to peripheral pain transduction. We aimed to investigate the impact of oestrogen receptors on the nociceptive neuronal response in the dorsal root ganglion (DRG) and spinal dorsal horn using a spared nerve injury (SNI) rat model of chronic pain. Methods: We intrathecally (i.t.) administered a class of oestrogen receptor antagonists and agonists intrathecal (i.t.) administrated to male rats with SNI or normal rats to identify the main receptor. Moreover, we assessed genes identified through genomic metabolic analysis to determine the key metabolism point and elucidate potential mechanisms mediating continuous neuronal sensitization and neuroinflammatory responses in neuropathic pain. The excitability of DRG neurons was detected using the patch-clamp technique. Primary culture was used to extract microglia and DRG neurons, and siRNA transfection was used to silence receptor protein expression. Immunofluorescence, Western blotting, RT-PCR and behavioural testing were used to assess the expression, cellular distribution, and actions of the main receptor and its related signalling molecules. Results: Increasing the expression and function of G protein-coupled oestrogen receptor (GPER), but not oestrogen receptor-α (ERα) and oestrogen receptor-β (ERβ), in the DRG neuron and microglia, but not the dorsal spinal cord, contributed to SNI-induced neuronal sensitization. Inhibiting GPER expression in the DRG alleviated SNI-induced pain behaviours and neuroinflammation by simultaneously downregulating iNOS, IL-1β and IL-6 expression and restoring GABAα2 expression. Additionally, the positive interaction between GPER and β-alanine and subsequent β-alanine accumulation enhances pain sensation and promotes chronic pain development. Conclusion: GPER activation in the DRG induces a positive association between β-alanine with iNOS, IL-1β and IL-6 expression and represses GABAα2 involved in post-SNI neuropathic pain development. Blocking GPER and eliminating β-alanine in the DRG neurons and microglia may prevent neuropathic pain development.

The purpose of a flood control reservoir operation is to prevent flood damage downstream of the reservoir and the safety of the reservoir itself. When a single reservoir cannot provide enough storage capacity for certain flood control points downstream, cascade reservoirs should be operated together to protect these areas from flooding. In this study, for efficient use of the reservoir storage, an optimal flood control operation model of cascade reservoirs for certain flood control points downstream was proposed. In the proposed model, the upstream reservoirs with the optimal operation strategy were considered to reduce the inflow of the reservoir downstream. For a large river basin, the flood routing and time-lag cannot be neglected. So, dynamic programming (DP) combined with the progressive optimality algorithm (POA) method, DP-POA, was proposed. Thus, the innovation of this study is to propose a two-stage optimal reservoir operation model with a DP-POA algorithm to solve the problem of optimal co-operation of cascade reservoirs for multiple flood control points downstream during the flood season. The upper Yangtze River was selected as a case study. Three reservoirs from upstream to downstream, Xiluodu, Xiangjiaba and the Three Gorges reservoirs (TGR) in the upper Yangtze River, were taken into account. Results demonstrate that the two-stage optimization algorithm has a good performance in solving the cascade reservoirs optimization problem, because the inflow of reservoir downstream and the division volumes were largely reduced. After the optimal operation of Xiluodu and Xiangjiaba reservoirs, the average reduction of flood peak for all these 13 typical flood hydrographs (TFHs) is 13.6%. Meanwhile, the cascade reservoirs can also store much more storm water during a flood event, and the maximum volumes stored in those two reservoirs upstream in this study can reach 25.2 billion m³ during a flood event. Comprising the proposed method with the current operation method, results demonstrate that the flood diversion volumes at the flood control points along the river decrease significantly.

The distributed unit hydrograph (DUH) method has been widely used for flow routing in a watershed because it adequately characterizes the underlying surface characteristics and varying rainfall intensity. Fundamental to the calculation of DUH is flow velocity. However, the currently used velocity formula assumes a global equilibrium of the watershed and ignores the impact of time-varying soil moisture content on flow velocity, which thus leads to a larger flow velocity. The objective of this study was to identify a soil moisture content factor, which, based on the tension water storage capacity curve, was derived to investigate the response of DUH to soil moisture content in unsaturated areas. Thus, an improved distributed unit hydrograph, based on time-varying soil moisture content, was obtained. The proposed DUH considered the impact of both time-varying rainfall intensity and soil moisture content on flow velocity, assuming the watershed to be not in equilibrium but varying with soil moisture. The Qin River basin and Longhu River basin were selected as two case studies, and the synthetic unit hydrograph (SUH), the time-varying distributed unit hydrograph (TDUH) and the current DUH methods were compared with the proposed method. Then, the influence of time-varying soil moisture content on flow velocity and flow routing was evaluated, and results showed that the proposed method performed the best among the four methods. The shape and duration of the unit hydrograph (UH) were mainly related to the soil moisture content at the initial stage of a rainstorm, and when the watershed was approximately saturated, the grid flow velocity was mainly dominated by excess rainfall. The proposed method can be used for the watersheds with sparse gauging stations and limited observed rainfall and runoff data.

Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting.