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Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)