Abstract:

Our project demonstrates the application of K-means clustering to groundwater quality monitoring, offering a valuable tool for environmental assessment and management in urban areas. The zoning map provides stakeholders (e.g., farmers and households) with actionable insights into the distribution of groundwater quality, supporting decision-making for better water resource management.

For visiting the codes for this project, please visit the GitHub repository below:https://github.com/fatemehomidi78/water_Quality_Monitoring_K_means_Clustering

Abstract

Through rigorous experimentation and analysis, we evaluated the efficiency and effectiveness of our algorithm approach in solving the optimization problem. Our results not only demonstrate the versatility of this algorithms but also provide practical insights into the selection of appropriate parameters and strategies for tackling similar optimization challenges in the field.

For further exploration and access to our MATLAB and Python code implementations, please visit our GitHub repository at https://github.com/fatemehomidi78/Optimization_Algorithm_1402

Abstract:

This project serves as a demonstration of the application of mathematical modeling techniques for studying environmental systems. The developed model and scenarios can serve as valuable tools for further research and decision-making processes.

For visiting the codes for this project, please visit the GitHub repository below:

https://github.com/fatemehomidi78/Pollution_modelling_1401