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Exploration of the application of data mining and GIS to identify areas with high-quality groundwater suitable for drinking, particularly in arid and semi-arid regions where water scarcity is a concern. The research compares various decision tree algorithms to determine their effectiveness in classifying groundwater quality based on key parameters like hardness, pH, chloride, and electrical conductivity. Ultimately, the study finds that certain algorithms, like Random Forest and Ordinary Decision Tree, perform best with continuous data and produce reliable maps of groundwater quality distribution, aiding in water supply management decisions.

⏰ 🕐Timestamp ⏲ 🕰

00:00 - Introduction to the Groundwater Quality Challenge

02:20 - Understanding Water Quality Standards and Decision Support Systems

03:48 - Data Mining Algorithms and Parameter Selection

05:44 - Continuous vs. Categorized Data Analysis

07:53 - Mapping and Spatial Analysis Results

10:33 - Practical Applications and Future Implications

#water #waterengineering #education #sdg6 #waterforall #waterquality #datamining #machinelearning #AI #R #python