In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

In line with rapid economic development and accelerated urbanization, the increasing discharge of wastewater and agricultural fertilizer usage has led to a gradual rise in ammonia nitrogen levels in rivers. High concentrations of ammonia nitrogen pose a significant challenge, causing eutrophication and adversely affecting the aquatic ecosystems and sustainable utilization of water resources. Traditional ammonia nitrogen detection methods suffer from limitations such as cumbersome sample handling and analysis, low sensitivity, and lack of real-time and dynamic feedback. In contrast, automated monitoring and ammonia nitrogen prediction technologies offer more efficient methods and accurate solutions. However, existing approaches still have some shortcomings, including sample processing complexity, interference issues, and the absence of real-time and dynamic information feedback. Consequently, deep learning techniques have emerged as promising methods to address these challenges. In this paper, we propose the application of a neural network model based on Long Short-Term Memory (LSTM) to analyze and model ammonia nitrogen monitoring data, enabling high-precision prediction of ammonia nitrogen indicators. Moreover, through correlation analysis between water quality parameters and ammonia nitrogen indicators, we identify a set of key feature indicators to enhance prediction efficiency and reduce costs. Experimental validation demonstrates the potential of our proposed approach to improve the accuracy, timeliness, and precision of ammonia nitrogen monitoring and prediction, which could provide support for environmental management and water resource governance.

Water quality monitoring is crucial for public health and environmental protection, but traditional methods lack real-time accuracy. This study addresses this gap by combining feature augmentation methods (e.g., sliding window) with Principal Component Analysis (PCA) and a tailored Two-Layer Regularized Gated Recurrent Unit (TLR-GRU) model for efficient water quality prediction. Results demonstrate that the proposed framework significantly improves prediction accuracy, with R² increased by 7.78%, RMSE decreased by 27.36%, MAE by 36.71%, and MAPE by 45.08%. This approach offers a novel technical pathway for real-time environmental monitoring and water resource management.

Water quality monitoring is critical for public health, ecology, and economic sustainability, but traditional methods are limited by temporal-spatial coverage and cost, failing to meet real-time assessment needs. Deep learning for water quality prediction is often hindered by high complexity and noise in raw time series. This study aims to address the high complexity and noise of hydrological time series by proposing a prediction framework integrating sliding window feature enhancement, principal component analysis (PCA), and a two-layer regularized gated recurrent unit (TLR-GRU). The core goal is to achieve high-precision real-time prediction of four key water quality parameters (dissolved oxygen (DO), ammonia nitrogen (${\mathrm{NH}}_3\text{-}\mathrm{N}$), total phosphorus (TP), and total nitrogen (TN)) for aquaculture and irrigation. Sample entropy (SampEn, $m=2$, $r=0.2 \times \mathrm{std}\left(X\right)$), a univariate complexity metric capturing intra-series pattern repetition, quantifies time series regularity, showing sliding windows reduce SampEn by filtering transient noise while retaining ecological patterns. This optimization synergizes with TLR-GRU’s regularization (L2, Dropout) to avoid overfitting. A total of 4970 water quality records (2020–2023, 4 h sampling interval) were collected from a monitoring station in a typical aquaculture-irrigated water body. After dimensionality reduction via PCA, experimental results demonstrate that the TLR-GRU model outperforms six state-of-the-art deep learning models (e.g., TLD-LSTM, WaveNet) on both the base dataset and the sliding window-enhanced dataset. On the latter, DO and TP test set $R^2$ rise from 0.82 to 0.93 and 0.81 to 0.92, with RMSE decreasing by 49.4% and 55.6%, respectively. This framework supports water resource management, applicable to rivers and lakes beyond aquaculture. Future work will optimize the model and integrate multi-source data.

Lithium-ion battery remaining useful life (RUL) prediction is critical for the safe and efficient operation of energy storage systems. Traditional physics-based and empirical data-driven methods rely heavily on expert knowledge and predefined degradation models, limiting their generalizability across diverse operating conditions. In recent years, deep learning has emerged as a powerful alternative, automatically learning features from raw sensor data without requiring explicit electrochemical assumptions. However, comparative studies of deep learning models for RUL prediction remain fragmented due to inconsistent benchmarks and preprocessing protocols. To address this, we present a unified evaluation of 24 models from six architectural families using the NASA PCoE battery dataset, covering multiple temperatures and discharge currents. All models share standardized inputs and preprocessing, including moving average smoothing with a window of 45 cycles to attenuate measurement noise. Evaluation covers prediction accuracy (RMSE, MAE, R², MAPE, SMAPE, MedAE) and computational efficiency, with Pareto frontier analysis. Results show that WaveNet achieves the best overall performance (RMSE = 0.0194, R² = 0.9834), followed by TCN (RMSE = 0.0247) and CNN (RMSE = 0.0307), while performance varies across families depending on the specific architecture and data regime. This study provides, for the first time, a systematic benchmark and practical guidance for deep learning model selection in battery health management.