## 3 Using ML for Water Activities
### Reservoir and River Quality Management
#### 4.2. Factors Influencing Water Consumption and Water Quality  
* **Household water consumption**[288]: This study proposed a framework for reviewing and analyzing the literature on determinants of household water consumption, aiding in prioritizing determinants for future research and practical recommendations.
* **Surgu Stream water quality**[289]: This study evaluated the water quality of the Surgu Stream in Turkey, assessed its impact on soil and crop performance, and provided insights into water quality index and suitability classes for irrigation.
* **Groundwater monitoring with ML**[290]: This study reviewed ML algorithms for groundwater monitoring and highlighted the effectiveness of ML in monitoring groundwater characteristics.
* **Water consumption in Qatar**[291]: This study analyzed factors affecting water consumption in Qatar and identified temperature and population density as key influences on water consumption.
* **Environmentally friendly toilets**[292]: This study developed a novel mechanism to reduce water consumption in toilets, aiming to make flushing more environmentally friendly, potentially conserving global water and energy.
* **Predicting water connection leaks**[293]: This study used ML to predict water connection vulnerability to ruptures and leaks. Models showed potential for effective distribution network management.
* **Corporate water management practices**[294]: This study examined the impact of macro factors on corporate water management practices, and identified factors driving water management practices for leading, average, and laggard companies.
* **Water quality parameter modeling**[295]: This study modeled water quality parameters in a river basin using regression models and provided water quality distribution maps based on watershed features.
* **Factors influencing domestic water consumption**[296]: This study analyzed factors influencing domestic water consumption in Joinville, Brazil. Socioeconomic and building characteristics play a significant role in water consumption.
* **Groundwater dynamics and prediction**[297]: This study used ML recharge. Rainfall was identified as a key influencing factor for groundwater recharge.
* **Energy-efficient underwater sensor networks**[298]: This study proposed an energy-efficient approach for underwater wireless sensor networks, utilizing clustering and routing techniques for efficient energy usage.
* **Groundwater management in arid regions**[299]: This study assessed groundwater management in Kebili's complex terminal aquifer and provided suitability classes for irrigation based on groundwater quality.
* **Model-independent leak detection**[300]: This study introduced a model-independent approach for placing pressure sensors in water distribution networks. It utilized genetic algorithms for leak detection without a hydraulic model.
* **Variable-rate irrigation (VRI)**[301]: This study explored the development of variable-rate irrigation (VRI) technologies for precision water management in agriculture. It highlighted the need for further research and practical support information.
* **Groundwater quality in Ojoto**[302]: This study assessed the quality of drinking groundwater in Ojoto, Nigeria, using pollution and ecological risk indices. It identified areas with contaminated water and suitability for drinking.  
These clusters group the bullets based on their shared themes and topics, providing a more organized overview of the content.