Study of ecosystem service functions in typical receiving areas of the South-to-North Water Diversion Central Route based on a set of long time series.

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.

Salidroside induces mitochondrial dysfunction and ferroptosis to inhibit melanoma progression through reactive oxygen species production.

Reactive oxygen species (ROS) induces necroptotic and ferroptosis in melanoma cells. Salidroside (SAL) regulates ROS in normal cells and inhibits melanoma cell proliferation. This study used human malignant melanoma cells treated with SAL either alone or in combination with ROS scavenger (NAC) or ferroptosis inducer (Erastin). Through cell viability, wound healing assays, and a Seahorse analyze found that SAL inhibited cell proliferation, migration, extracellular acidification rate, and oxygen consumption rate. Metabolic flux analysis, complexes I, II, III, and IV activity of the mitochondrial respiratory chain assays, mitochondrial membrane potential assay, mitochondrial ROS, and transmission electron microscope revealed that SAL induced mitochondrial dysfunction and ultrastructural damage. Assessment of malondialdehyde, lipid ROS, iron content measurement, and Western blot analysis showed that SAL activated lipid peroxidation and promoted ferroptosis in A-375 cells. These effects were abolished after NAC treatment. Additionally, SAL and Erastin both inhibited cell proliferation and promoted cell death; SAL increased the Erastin sensitivity of cells while NAC antagonized it. In xenograft mice, SAL inhibited melanoma growth and promoted ROS-dependent ferroptosis. SAL induced mitochondrial dysfunction and ferroptosis to block melanoma progression through ROS production, which offers a scientific foundation for conducting SAL pharmacological research in the management of melanoma.

Progressive Facial Ulcer: A Case Report of Pyoderma gangrenosum.

Pyoderma gangrenosum (PG) is a rare neutrophilic dermatosis characterized by rapidly developing and painful skin ulcers with distinctive features. As far as we are concerned, there is no previous case report on facial PG in East-Asia. In this case, we describe a case of a 79-year-old man with a 3-month history of progressive painful ulcers on his cheek and upper lip. Initial suspicion of atypical mycobacterium infection led to an ineffective treatment regimen. Comprehensive infectious testing yielded negative results, and a positive pathergy test indicated a potential diagnosis of PG. A skin biopsy confirmed the diagnosis, and the patient showed significant improvement with intravenous methylprednisolone and oral cyclosporine treatment. After three months, complete resolution of the lesions was achieved without recurrence. The case highlights the diagnostic challenges associated with PG, which is often misdiagnosed due to its resemblance to other conditions. Thorough evaluation is crucial to exclude alternative diagnoses, particularly cutaneous infections. Clinical morphology, tissue biopsy, and culture are essential for accurate diagnosis. The presence of pathergy, the development of new lesions following minor trauma, can also be a diagnostic clue.

A monthly temperature prediction based on the CEEMDAN-BO-BiLSTM coupled model.

Temperature as an important indicator of climate change, accurate temperature prediction has important guidance and application value for agricultural production, energy management and disaster warning. Based on the advantages of CEEMDAN model in effectively extracting the time-frequency characteristics of nonlinear and nonsmooth signals, BO algorithm in optimizing the objective function within a limited number of iterations, and BiLSTM model in revealing the connection between the current data, the previous data and the future data, a monthly average temperature prediction model based on CEEMDAN-BO-BiLSTM is established. A CEEMDAN-BO-BiLSTM-based monthly average temperature prediction model is developed and applied to the prediction of monthly average temperature in Jinan City, Shandong Province. The results show that the constructed monthly mean temperature prediction model based on CEEMDAN-BO-BiLSTM is feasible; the constructed CEEMDAN-BO-BiLSTM model has an average absolute error of 1.17, a root mean square error of 1.43, an average absolute percentage error of 0.31%, which is better than CEEMDAN-BiLSTM, EMD-BiLSTM, and BiLSTM models in terms of prediction accuracy and shows better adaptability; the constructed CEEMDAN-BO-BiLSTM model illustrates that the model is not over-modeled and adds complexity using Friedman's test and performance comparisons between model run speeds. The model provides insights for effective forecasting of monthly mean temperatures.

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.

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.

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.

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.

Characteristics of ecosystem service values in response to strong human activities in water sources of the South-to-North Water Diversion Middle Route in China.

A systematic grasp of the evolution of the spatial and temporal patterns of ecosystem service value (ESV) in the Central Line Project for South-to-North Water Diversion (CLPSNWD) water source area is conducive to deepening the ecological protection and promoting high-quality development of the water source area. In this paper, the dynamically adjusted equivalent factor method is used to reveal the spatial and temporal evolution of ESV in the water source area under strong human activities from 1991 to 2020. The results show that (1) during the 30-year period, urban point expansion increased the construction land area by 63.66 km2, and the degree of fragmentation increased. The water area increased the most, reaching 209.43 km2. (2) The total increase in ESV over the 30-year period was $1434 million, with forests and water accounting for the largest increase, i.e., 98% of the total increase in value. Among the individual service functions, hydrologic regulation generated the most significant service value.

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin.

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 R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 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.

Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy.

Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28-1 and 0.38-0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%.

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model.

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 (R2) 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.

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.

Improvement and evaluation of hydrodynamic conditions in plain regional drainage systems by artificial lakes: a case study of Xinxiang Economic Development Zone, China.

With the development of the city, people pay more attention to the ecological construction of the city. The objective of this work was to study the effect of artificial lakes on hydrodynamic conditions in urban drainage systems. With Arcgis and the advantage of SWMM in analyzing the impact of the rainfall process on urban runoff, the urban flooding model of "pipe network + river network + artificial lake" was established in the study area. Two scenarios were set up with and without the presence of artificial lakes, and comparative analyses were conducted under the different intensities of rainfall (0.5a, 1a, 2a, 5a, 10a, 20a). The results show that under certain rainfall conditions, the presence of the artificial lake increases the peak flow and rate of upstream streams and decreases the flow and rate of downstream streams in the regional drainage system. The duration of the peak flow rate in the upstream channel increases, and the flow rate curve becomes flat during the confluence; the flow rate in the downstream section decreases, and the magnitude of the peak flow rate change decreases, and a more obvious horizontal section appears. The time of peak occurrence in the downstream river is earlier. The hydrodynamic impact on the downstream channel is more significant. The improvement of hydrodynamic conditions of the drainage system by the artificial lake helps to optimize the layout of low impact development (LID) measures in the study area and also guides ecological construction in other cities.

Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis.

Introduction: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue. Methods: Our approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells. Results: The image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis. Discussion: The image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration.

Quantitative Analysis of the Influence of the Xiaolangdi Reservoir on Water and Sediment in the Middle and Lower Reaches of the Yellow River.

The Xiaolangdi Reservoir is the second largest water conservancy project in China and the last comprehensive water conservancy hub on the mainstream of the Yellow River, playing a vital role in the middle and lower reaches of the Yellow River. To study the effects of the construction of the Xiaolangdi Reservoir (1997-2001) on the runoff and sediment transport in the middle and lower reaches of the Yellow River, runoff and sediment transport data from 1963 to 2021 were based on the hydrological stations of Huayuankou, Gaocun, and Lijin. The unevenness coefficient, cumulative distance level method, Mann-Kendall test method, and wavelet transform method were used to analyze the runoff and sediment transport in the middle and lower reaches of the Yellow River at different time scales. The results of the study reveal that the completion of the Xiaolangdi Reservoir in the interannual range has little impact on the runoff in the middle and lower reaches of the Yellow River and a significant impact on sediment transport. The interannual runoff volumes of Huayuankou station, Gaocun station, and Lijin station were reduced by 20.1%, 20.39%, and 32.87%, respectively. In addition, the sediment transport volumes decreased by 90.03%, 85.34%, and 83.88%, respectively. It has a great influence on the monthly distribution of annual runoff. The annual runoff distribution is more uniform, increasing the runoff in the dry season, reducing the runoff in the wet season, and bringing forward the peak flow. The runoff and Sediment transport have obvious periodicity. After the operation of the Xiaolangdi Reservoir, the main cycle of runoff increases and the second main cycle disappears. The main cycle of Sediment transport did not change obviously, but the closer it was to the estuary, the less obvious the cycle was. The research results can provide a reference for ecological protection and high-quality development in the middle and lower reaches of the Yellow River.

A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN.

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.

A coupled CEEMD-BiLSTM model for regional monthly temperature prediction.

Temperature is an important indicator of climate change. With the gradual increase of global warming, a well-chosen model can improve the accuracy of temperature prediction. It is of great significance and value for future disaster prevention and mitigation and economic development. Monthly temperature is influenced by solar activity, monsoon, and other factors, with significant uncertainty, ambiguity, and randomness. A coupled CEEMD-BiLSTM temperature model is constructed based on the good decomposition-reconstruction characteristics of CEEMD for uncertain time series and the advantages of BiLSTM for solving stochastic prediction, and it is applied to the prediction of monthly temperature in Zhengzhou City. The results show that the minimum relative error of the coupled CEEMD-BiLSTM model is 0.01%, the maximum relative error is 0.99%, and the average relative error is 0.22%, and the prediction accuracy of this coupled model for monthly temperature in Zhengzhou is higher than that of the CEEMD-LSTM model, EEMD-BiLSTM model, and BP neural network model, with better stability and adaptability.