Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes.

Water pollution has caused serious consequences for human health and aquatic systems. Therefore, analyzing and predicting water quality is of great significance for the early prevention and control of water pollution. Aiming at the shortcomings of the Gated Recurrent Unit (GRU) water quality prediction model, such as the low utilization rate of early information and poor deep feature extraction ability of the hidden state mechanism, this study combines the temporal attention (TA) mechanism with the bidirectional superimposed neural network. A time-focused bidirectional gated recurrent unit (TA-Bi-GRU) model is proposed. Taking the actual water quality data of the water source reservoir in Xiduan Village as the research object, this model was used to predict four core water quality indicators, namely pH, ammonia nitrogen (NH₃N), total nitrogen (TN), and dissolved oxygen (DOX). Predictions are made within multiple time ranges, with prediction periods of 7 days, 10 days, 15 days, and 30 days. In the long-term prediction of the TA-Bi-GRU model, its average R² was 0.858 (7 days), 0.772 (10 days), 0.684 (15 days), and 0.553 (30 days), and the corresponding average MAE and MSE were both lower than those of the comparison models. The experimental results show that the TA-Bi-GRU model has higher prediction accuracy and stronger generalization ability compared with the existing GRU, bidirectional GRU (Bi-GRU), Time-focused Gated Recurrent Unit (TA-GRU), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Deep Temporal Convolutional Networks-Long Short-Term Memory (DeepTCN-LSTM) models.

To address the problems of noise sensitivity, insufficient modeling of long-term time-series dependence, and high cost of labeled data in the fault diagnosis of pumping station units, an intelligent diagnosis method integrating the improved Sparrow Search Algorithm (tSSA) and Informer model is proposed in this study. Firstly, an adaptive t-distribution strategy is introduced into the Sparrow Search Algorithm to dynamically adjust the degree of freedom parameters of the mutation operator, balance global search and local development capabilities, avoid the algorithm converging to the origin, and enhance optimization accuracy, with time complexity consistent with the original SSA. Secondly, by combining the sparse self-attention and self-attention distillation mechanisms of Informer, the model’s ability to extract key features of long sequences is optimized, and its hyperparameters are adaptively optimized via tSSA. Experiments were conducted based on 12 types of fault vibration data acquired from pumping station units. Outliers were removed using the interquartile range (IQR) method, and dimensionality reduction was achieved through kernel principal component analysis (KPCA). The results indicate that the average diagnostic accuracy of tSSA-Informer under noise-free conditions reaches 98.73%, which is significantly higher than that of models such as SSA-Informer and GA-Informer; under noise interference of SNR = −1 dB, it still maintains an accuracy of 87.47%, outperforming comparative methods like 1D-DCTN; when the labeled sample size is reduced to 10%, its accuracy is 61.32%, which is more than 40% higher than that of traditional models. These results verify the robustness and practicality of the proposed method in strong-noise and small-sample scenarios. This study provides an efficient solution for the intelligent fault diagnosis of complex industrial equipment.

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy.

Accurate monthly runoff simulation is the foundation of watershed water resources management and flood–drought disaster prevention. To address the issues that traditional machine learning models struggle to capture long-term dependencies in runoff series and that existing deep learning methods ignore the differences in driving mechanisms across intra-annual hydrological periods, this paper proposes a Period-Adaptive Attention Informer (PA-Informer), in which a learnable period embedding is introduced into the attention mechanism to achieve adaptive representation of the memory length of each period. Based on autocorrelation analysis, K-means clustering and dynamic time warping, the hydrological year is divided into a dry period (November–February), a transition period (March–June and October) and a wet period (July–September). Using monthly precipitation, monthly mean temperature and antecedent runoff (lags 1–3) observed at the Changtaiguan Hydrological Station in the Chushandian Irrigation Area of the upper Huaihe River (1960–2020), a one-month-ahead monthly runoff simulation is performed with the meteorological variables of the target month taken as known; the record is split chronologically into a training set (1960–2002) and a test set (2003–2020) at a ratio of 7:3, and all preprocessing and tuning procedures are restricted to the training set. On the test set, PA-Informer achieves an R² = 0.957, NSE = 0.948, RMSE = 0.71 m³/s and MAE = 0.52 m³/s (mean over five random seeds), outperforming the best benchmark (Base-Informer) with a statistically significant difference (Wilcoxon signed-rank test, p = 0.0023). SHAP analysis reveals that dry-period runoff is driven mainly by temperature and antecedent runoff, transition-period runoff by precipitation and long-lag runoff, and wet-period runoff predominantly by precipitation with temperature acting as a suppressor. PA-Informer effectively integrates the physical laws of hydrological periodicity with a deep learning architecture, providing a new approach for high-accuracy monthly runoff simulation in irrigation areas.

Accurate prediction of crop water requirements is key to efficient water resource scheduling and agricultural irrigation management in irrigation districts. Taking the Chushandian Reservoir Irrigation District as the study area, this research calculated crop water requirements using the Penman-Monteith formula and crop coefficient method, based on monthly meteorological data (1981–2020) from the Changtaiguan hydrological station and five surrounding national meteorological stations (Zhumadian, Xinyang, Queshan, Zhengyang, and Luoshan), along with crop coefficients for six major crops (wheat, rapeseed, corn, peanut, rice, and vegetables). The water requirement distribution across different growth stages was then analyzed, and XGBoost-based feature importance ranking combined with Pearson correlation analysis was used to identify the key meteorological factors influencing crop water requirements. Finally, a bidirectional gated recurrent unit incorporating a temporal attention mechanism (TA-Bi-GRU) was constructed and compared with MGMA-LSTM, GCRA-TCN, and LSTM-CNN in both joint prediction and crop-specific independent prediction experiments. The results showed that vegetables had the highest total water requirement over the full growth period (656.79 mm), followed by rice (475.04 mm), and rapeseed the lowest (260.05 mm); the net irrigation water requirement of each crop peaked during the mid-growth stage. Relative humidity, sunshine hours, and maximum temperature were identified as the top three meteorological factors affecting crop water requirements, with a cumulative importance exceeding 75%. In the joint prediction task, TA-Bi-GRU achieved an R² of 0.9487 and an RMSE of 0.2257 mm, representing the best numerical performance among the four evaluated deep-learning models. In the crop-specific independent prediction task, TA-Bi-GRU achieved R² values of no less than 0.9024 across the six crops. By integrating bidirectional temporal representation with temporal attention, TA-Bi-GRU shows potential as a data-driven tool for monthly crop-water-requirement prediction and irrigation decision support in the studied district.