IOT BASED SMART WATER MANAGEMENT SYSTEM

Water scarcity is becoming a critical global issue due to rapid population growth, climate change, and 
increasing urbanization. Efficient water resource management requires accurate forecasting of water demand 
to ensure sustainable supply and distribution. However, traditional forecasting methods often fail to capture 
complex, nonlinear, and dynamic water consumption patterns influenced by multiple factors. To address this 
limitation, this study utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically 
Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models, to improve prediction 
accuracy. 
The proposed models analyze historical water consumption data along with meteorological, socioeconomic, 
and temporal variables to forecast future demand more effectively. AI models are capable of identifying 
hidden patterns, seasonal variations, and long-term dependencies in water usage. This enables more precise 
and reliable prediction compared to conventional statistical approaches. The performance of the models is 
evaluated using standard accuracy metrics such as Mean Absolute Error (MAE) and Root Mean Square Error 
(RMSE), which demonstrate the effectiveness of AI-based methods. 
The results indicate that ANN and LSTM models significantly enhance forecasting accuracy and reliability. 
These predictive systems help water authorities optimize distribution, reduce wastage, and improve planning. 
Overall, AI-based water demand forecasting supports sustainable water management and ensures efficient 
utilization of water resources.