A novel optimization rainfall coupling model based on stepwise decomposition technique.

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

A runoff prediction method based on hyperparameter optimisation of a kernel extreme learning machine with multi-step decomposition.

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 m3/s, 18.79 m3/s, 18.66 m3/s, and 21.87 m3/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.

Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN-SE-BiLSTM model.

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 (R2) is increased by 0.0208, 0.1265, 0.1381.

Deep learning precipitation prediction models combined with feature analysis.

Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.

Comparative study of rainfall prediction based on different decomposition methods of VMD.

Rainfall forecasting is an important means for macro-control of water resources and prevention of future disasters. In order to achieve a more accurate prediction effect, this paper analyzes the applicability of the "full decomposition" and "stepwise decomposition" of the VMD (Variational mode decomposition) algorithm to the actual prediction service; The MAVOA (Modified African Vultures Optimization Algorithm) improved by Tent chaotic mapping is selected; and the DNC (Differentiable Neural Computer), which combines the advantages of recurrent neural networks and computational processing, is applied to the forecasting. The different VMD decompositions of the MAVOA-DNC combination together with other comparative models are applied to example predictions at four sites in the Huaihe River Basin. The results show that SMFSD (Single-model Fully stepwise decomposition) is the most effective, and the average Root Mean Square Error (RMSE) of the forecasts for the four sites of SMFSD-MAVOA-DNC is 9.02, the average Mean Absolute Error (MAE) of 7.13, and the average Nash-Sutcliffe Efficiency (NSE) of 0.94. Compared with the traditional VMD full decomposition, the RMSE is reduced by 7.42, the MAE is reduced by 4.83, and the NSE is increased by 0.05; the best forecasting results are obtained compared with other coupled models.

Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach.

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, R2 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.

Daily runoff prediction during flood seasons based on the VMD-HHO-KELM model.

Improving the accuracy of daily runoff in the lower Yellow River is important for flood control and reservoir scheduling in the lower Yellow River. Influenced by factors such as meteorology, climate change, and human activities, runoff series present non-stationary and non-linear characteristics. To weaken the non-linearity and non-smoothness of runoff time series and improve the accuracy of daily runoff prediction, a new combined runoff prediction model (VMD-HHO-KELM) based on the ensemble Variational Modal Decomposition (VMD) algorithm and Harris Hawk Optimisation (HHO) algorithm-optimised Kernel Extreme Learning Machine (KELM) is proposed and applied to Gaocun and Lijin hydrological stations. The VMD-HHO-KELM model has the highest prediction accuracy, with the prediction model R2 reaching 0.95, mean absolute error reaching 13.3, and root mean square error reaching 33.83 at the Gaocun hydrological station, and R2 reaching 0.96, mean absolute error reaching 8.03, and root mean square error reaching 38.45 at the Lijin hydrological station.

Hyaluronic acid modified nanostructured lipid carriers for transdermal bupivacaine delivery: In vitro and in vivo anesthesia evaluation.

For effective transdermal local anesthetic therapy, to reduce the barrier of stratum corneum and improve the antinociceptive effect, hyaluronic acid (HA) modified, bupivacaine (BPV) loaded nanostructured lipid carriers (NLCs) were designed. HA and linoleic acid (LOA) conjugated propylene glycol (PEG) was synthesized (HA-PEG-LOA). HA-PEG-LOA was added during the preparation process of NLCs, thus LOA was inserted into the NLCs, The physicochemical properties of NLCs, particle size, zeta potential, drug loading capacity, in vitro skin permeation, drug release profiles and in vivo therapeutic effect were evaluated. HA-BPV/NLCs have small particle size of 150?nm, with a zeta potential of ?40?mV. Nearly 90% high drug encapsulation efficiency and good stability were also observed. In vitro release rate of BPV from HA-BPV/NLCs was complying with a sustained behavior until 72?h of study. HA-BPV/NLCs and BPV/NLCs exhibited 2.5 and 1.6 fold of percutaneous penetration improvement than free BPV. BPV loaded NLCs produced a more prolonged antinociceptive effect when compared with free BPV. In vitro and in vivo results pointed out HA modified NLCs have the capability to act as effective drug carriers, thus prolonging and enhancing the anesthetic effect of BPV. The NLCs developed in this study might provide a useful platform for developing a sophisticated dermal delivery system for analgesic.