Quantifying uncertainty of marine water quality forecasts for environmental management using a dynamic multi-factor analysis and multi-resolution ensemble approach

Data-driven identification of pollution sources and water quality prediction using Apriori and LSTM models: A case study in the Hanjiang River basin

The rapid development of urbanization and industrialization has exacerbated surface water pollution, especially from point sources such as industrial discharge and urban wastewater, posing a severe challenge to global environmental health and sustainable development. This study combines the Apriori algorithm and Long Short-Term Memory (LSTM) networks to identify major pollution sources and predict dynamic changes in water quality. The study area encompasses four national monitoring hydrological stations in the core area of the South-to-North Water Diversion Project, with multi-source data collected, including water quality parameters and industry-specific discharge data. Using the Apriori algorithm, the pollutants with the highest support-chemical oxygen demand (COD), copper (Cu), suspended solids (SS), and zinc (Zn)-demonstrated a support value of 0.87, indicating that the metallurgical, electroplating, and chemical industries are the primary pollution sources. Further association rule analysis based on varying parameter thresholds revealed that when COD is present, the co-occurrence confidence for Cadmium (Cd), Cu, Lead (Pb), and SS reaches 0.9, and the combination of COD, Cu, Pb, SS, and Cyanide (CN) achieves a confidence level of 1, indicating a high degree of correlation among these pollutants. The LSTM model demonstrated high accuracy in water quality prediction, with Root Mean Square Error (RMSE) values for COD predictions at each hydrological station ranging from 0.2076 to 0.3366, and coefficients of determination (R2) all exceeding 0.9, highlighting the model's stability and predictive accuracy. This study provides a scientific basis for the sustainable management of watershed water resources and serves as a significant reference for environmental policymaking and water resource protection.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.