Application of Data Mining Techniques for Developing an Information System for Water Demand Forecasting
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Abstract
This study aims to compare the performance of water demand forecasting models in Thailand using data mining techniques under the CRISP-DM framework. The models include Linear Regression, Artificial Neural Network, Random Forest, and Gradient Boosted Trees. Monthly water consumption data from 2014 to 2023 were obtained from the national open data platform and divided into training, validation, and testing sets at a ratio of 80:10:10. RapidMiner was used for model development. Performance evaluation was conducted using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show that Linear Regression achieved the best performance with an MAE of 188,158.96 m³ and an RMSE of 310,809.74 m³, which were lower than those of the other models and demonstrated high stability. A web-based prototype system was also developed to visualize forecasting results through dashboards, supporting water production planning and distribution management in alignment with Sustainable Development Goal 6.
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