Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

Characteristics and Causes of Long-Term Water Quality Variation in Lixiahe Abdominal Area, China

The Lixiahe abdominal area is a representative plain river network in the lower reaches of the Huai River, being an upstream section of south-to-north water diversion from the Yangtze River in Jiangsu Province, China. The assessment of long-term water quality variation and the identification of probable causes can provide references for sustainable water resources management. Based on the monthly water quality data of 15 monitoring stations in the Lixiahe abdominal area, the periodic characteristics and tendency of water quality variation were studied by combining wavelet analysis, the Mann–Kendall trend test, and Sen’s slope estimator, and the correlation between water quality variation, water level, and water diversion was discussed with cross wavelet transform and wavelet coherence. The results show that the comprehensive water quality index (CWQI) included periodic fluctuations on multiple scales from 0.25 to 5 years. The CWQI of 7 out of 15 monitoring stations has a significant decreasing trend, indicating regional water quality improvement. The trend slope ranges from −0.071/yr to 0.007/yr, where −0.071/yr indicates the water quality improvement by one grade in 15 years. The spatial variation of water quality in the Lixiahe abdominal area was significant. The water quality of the main water diversion channels and its nearby rivers was significantly improved, while the improvement of other areas was not significant or even became worse due to the increasing discharge of pollutants. The CWQI of the main water diversion channels and its nearby rivers was inversely correlated with the amount of water diversion. The greater the amount of water diversion, the better the water quality. The water diversion from the Yangtze River has played an important role in improving the regional water environment.