Smart City Gnosys
Smart city article details
| Title | Ensemble Deep And Machine Learning For Improving Short-Term Water Demand Forecast In Cities |
|---|---|
| ID_Doc | 24110 |
| Authors | Toto P.; Kimwele M.; Rimiru R. |
| Year | 2024 |
| Published | 2024 IST-Africa Conference, IST-Africa 2024 |
| DOI | http://dx.doi.org/10.23919/IST-Africa63983.2024.10569729 |
| Abstract | Intelligent water distribution systems are essential to ensure water availability for all city users amidst increasing water scarcity driven by urbanization and climate change. In this study, our goal was to enhance the forecasting accuracy of a water utility using a Bayesian Moving Averaging (BMA) ensemble model. This model combines Random Forest (RF), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP) algorithms. We utilized daily monitoring data from the water utility spanning from 2018 to 2023 to predict short-term water demand, incorporating weather features. Our findings demonstrate that the proposed ensemble model achieved the best performance, yielding the lowest mean absolute percentage error (MAPE) of 15.99% and the highest R squared (R2) value of 0.98 on the testing set. Additionally, the RF model exhibited better results compared to the other single models. These outcomes underscore the potential of the ensemble approach in short-term water demand forecasting, surpassing more traditional and state-of-the-art methods. © 2024 IST-Africa Institute and Authors. |
| Author Keywords | deep learning; ensemble model; IoT; machine learning; smart cities; Water demand prediction |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
54126  | 0.881 | Ubaid A.; Lin X.; Hussain F.K. | Sw Forecaster: An Intelligent Data-Driven Approach For Water Usage Demand Forecasting | Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom (2024) |
| 3229 | 0.878 | Xu Z.; Lv Z.; Li J.; Shi A. | A Novel Approach For Predicting Water Demand With Complex Patterns Based On Ensemble Learning | Water Resources Management, 36, 11 (2022) |
| 34624 | 0.859 | Wu S.; Wang J.; Xu H.; Zhao S.; Xu J. | Knowledge-Based Bi-Correction Model For Achieving Effective Lag-Free Characteristic On Daily Urban Water Demand Forecasting | Expert Systems with Applications, 255 (2024) |
| 10905 | 0.857 | Bui M.T.; Yáñez-Godoy H.; Elachachi S.M. | Assessment Of The Implications And Challenges Of Using Artificial Intelligence For Urban Water Networks In The Context Of Climate Change When Building Future Resilient And Smart Infrastructures | Journal of Pipeline Systems Engineering and Practice, 16, 1 (2025) |
| 61524 | 0.855 | Habelalmateen M.I.; Palanivel R.; Rana Veer Samara Sihman Bharattej R.; Vijayashaarathi S.; Poonguzhali I. | Wavelet Transform Based Bidirectional Long Short-Term Memory For Predicting Water Demand | 2024 1st International Conference on Software, Systems and Information Technology, SSITCON 2024 (2024) |
| 38699 | 0.854 | Wang Q.; Wang P.; Cai M. | Multivariate Time Series Forecasting Of Daily Urban Water Demand Using Reinforcement Learning And Gated Recurrent Unit Network | ACM International Conference Proceeding Series (2024) |