Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling

W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.

Hydrochemical characteristics and the impact of human activities on groundwater in a semi-arid plain: a case study of western Jilin Province, Northeast China

Groundwater is important for human survival and development, particularly in arid and semi-arid regions. This study aimed to analyze the hydrochemical characteristics, influencing factors, and the impact of human activities on groundwater in the semi-arid plains of western Jilin Province, northwest China. The study collected 88 and 151 phreatic and confined water samples, respectively, which were analyzed for 13 water quality indicators using statistical and graphical methods. In order to investigate the impact of anthropogenic activities on water quality and health risks, the improved combined weighted water quality index (ICWQI) based on the entropy weight, criteria importance though inter-criteria correlation (CRITIC), the coefficient of difference method, subjective weight based on quality grading criteria, and the water quality index (WQI) were proposed to evaluate the water quality of the study area. Meanwhile, the human health risk assessment (HHRA) model was used to assess the risks of nitrate to the health of humans in different ages and sex categories. The results indicated that the groundwater in the study area was weakly alkaline and the main hydrochemical types in the phreatic and confined water were HCO3-·Ca-Mg and HCO3--Na. Rock weathering was the dominant process responsible for the generation of groundwater ions, the ions in groundwater primarily originate from the dissolution of halite, gypsum, and feldspar, while dolomitization promotes an increase in Mg2+. Human activities lead to an increase in NO3- in groundwater and have an impact on water quality and human health risks. The ICWQI method was found to yield more precise and rational assessments of water quality. Groundwater quality is primarily affected by nitrate ions. The areas in which groundwater nitrate posed a higher risk to human health were found to be mainly in the saline-alkali lands of Qian'an, Tongyu, and Zhenlai. Fertilizers, pesticides, and livestock farming activities contribute to the pollution of surface water. This surface contamination then infiltrates abandoned confined wells, leading to contamination of the confined aquifers. This study can improve the understanding of groundwater hydrochemical characteristics and the impact of human activities on groundwater in the study area. This study can also contribute to the study of groundwater in semi-arid regions.

Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO₂, O₃, NO₂, CO₂, CO, and CH₄ values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 °C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O₂ in water (R_TOC²=0.98), [Formula: see text] , R_COD²=0.87 and O₃, NO₃, CO₂, and CO in the air ( [Formula: see text] ). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R² = 0.98).

Hybridization of long short-term memory with Sparrow Search Optimization model for water quality index prediction

Water quality (WQ) analysis is a critical stage in water resource management and should be handled immediately in order to control pollutants that could have a negative influence on the ecosystem. The dramatic increase in population, the use of fertilizers and pesticides, and the industrial revolution have resulted in severe effects on the WQ environment. As a result, the prediction of WQ greatly helped to monitor water pollution. Accurate prediction of WQ is the foundation of managing water environments and is of high importance for protecting water environment. WQ data presents in the form of multi-variate time-sequence dataset. It is clear that the accuracy of predicting WQ will be enhanced when the multi-variate relation and time sequence dataset of WQ are fully utilized. This article presents the Water Quality Prediction utilising Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM) model. The presented WQP-SSHLSTM model intends to examine the data and classify WQ into distinct classes. To achieve this, the presented WQP-SSHLSTM model undergoes data scaling process to scale the input data into uniform format. Followed by, a hybrid long short-term memory-deep belief network (LSTM-DBN) technique is employed for the recognition and classification of WQ. Moreover, Sparrow search optimization algorithm (SSOA) is utilized as a hyperparameter optimizer of the proposed DBN-LSTM model. For demonstrating the enhanced outcomes of the presented WQP-SSHLSTM model, a sequence of experiments has been performed and the outcomes are reviewed under distinct prospects. The WQP-SSHLSTM model has achieved 99.84 percent accuracy, which is the maximum attainable. The simulation outcomes ensured the enhanced outcomes of the WQP-SSHLSTM model on recent methods.

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.