## 3 Using ML for Water Activities
### Reservoir and River Quality Management  
#### 3.4.1. Innovations in Water Resource Management and Quality  
* **Reservoir operation optimization**[273]: This study presented a classification system for organizing literature on reservoir operation optimization, providing practical recommendations.
* **Soft computing for water quality index (WQI)**[274]: This study developed a WQI using soft computing techniques, with ANFIS demonstrating reliability for WQI prediction.
* **Groundwater quality assessment in Sri Lanka**[275]: This study assessed the quality of groundwater for irrigation in Sri Lanka's tank cascade system, suggesting suitable areas.
* **Combatting reservoir sedimentation**[276]: This study categorized strategies for combating reservoir sedimentation, offering a checklist for evaluating sediment management options.
* **Underground water level prediction**[277]: This study utilized a hybrid model to predict underground water levels in Khuzestan province, achieving high accuracy in water resource modeling.
* **River water quality modeling**[278]: This study utilized AI models to predict river water quality index (WQI) based on water quality variables. H2O deep learning and random forest models were effective, especially for small catchments.
* **Superabsorbent hydrogel (SH)**[279]: This study explored the application of superabsorbent hydrogel (SH) in agriculture and slow-release fertilizers and discussed nutrient release mechanisms, highlighting the potential for sustainable agriculture.
* **Urbanization and groundwater quality**[280]: This study examined the impact of urbanization and land use on groundwater quality in Xi'an City, China, supporting sustainable urban development and groundwater management in Xi'an City.
* **Leakage detection**[281]: This study focused on efficient leakage detection in water distribution systems and emphasized the importance of enhancing operational efficiency and minimizing water losses.
* **ML in water systems**[282]: This study explored the application of ML in natural and engineered water systems and discussed the advantages and disadvantages of various ML algorithms for water-related issues.
* **Groundwater nitrate contamination**[283]: This study utilized ML techniques to predict nitrate concentrations in Mexico's groundwater and identified pollution hotspots and health concerns, emphasizing the need for sustainable agricultural practices.
* **Smart water management**[284]: This study discussed how smart water meters and data analytics improve urban water system design and highlighted enhanced efficiency throughout the water cycle.
* **River water quality prediction**[285]: This study developed ML models to predict river water quality and classify index values and achieved efficient prediction and classification of water quality index values.
* **Flow-regime-dependent streamflow prediction**[286]: This study proposed a flow-regime-dependent approach using various techniques to improve streamflow prediction, enhancing streamflow prediction for water resources management and planning.  
Water use and management indicators [287]: This study evaluated water use and management indicators based on sustainability criteria and identified indicators meeting sustainability criteria for informed decision making.