Published Paper Details:

Access to clean water and proper hygiene facilities is a fundamental human right and a critical determinant of public health, economic stability, and social development. Despite global efforts, millions of people, especially in rural and underserved regions, continue to face significant challenges in accessing safe drinking water and adequate sanitation. Identifying and addressing these issues is complex due to varying geographical, demographic, and infrastructural factors that influence water availability and hygiene standards. This study presents an innovative machine learning-driven approach to analyze and predict water access and hygiene conditions based on a comprehensive dataset encompassing key determinants such as population density, geographical features, water source type, sanitation infrastructure, and socio-economic factors. By employing advanced data analytics and predictive modeling, our research aims to uncover patterns and relationships within these variables, enabling the proactive identification of regions at risk of water scarcity and poor sanitation. The system utilizes various machine learning algorithms, including decision trees, random forests, support vector machines (SVM), and deep learning models, to enhance prediction accuracy and provide actionable insights. These models are trained and validated using diverse real-world datasets, ensuring robustness and generalizability. The integration of machine learning in water resource management provides several advantages, including real-time monitoring, automated risk assessment, and data-driven decision-making. The findings from this study will assist policymakers, non-governmental organizations (NGOs), environmental agencies, and urban planners in making informed decisions regarding water infrastructure investments, hygiene awareness programs, and emergency response strategies. Furthermore, this research contributes to the United Nations' Sustainable Development Goal (SDG) 6, which aims to ensure clean water and sanitation for all by 2030.