Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea
Abstract
:1. Introduction
2. Study Process and Methodology
2.1. Study Process
2.2. Methodology
2.2.1. ARIMA
2.2.2. RBF-ANN
2.2.3. QMMP+
2.2.4. LSTM
2.3. Performance Assessment
3. Study Area and Dataset Description
3.1. Study Area
3.2. Dataset Description
4. Results and Discussion
4.1. AMIs Water Demand Forecasting
4.2. Total Water Demand Forecasting
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- MOLIT. Smart Water Grid: Global Leader Korea’s Water Management Technology; SWGRG, MOLIT: Sejong, Korea, 2017. [Google Scholar]
- Fikejz, J.; Roleček, J. Proposal of a smart water meter for detecting sudden water leakage. In Proceedings of the 2018 ELEKTRO, Mikulov, Czech Republic, 21–23 May 2018; pp. 1–4. [Google Scholar]
- Koo, D.; Piratla, K.; Matthews, C.J. Towards sustainable water supply: Schematic development of big data collection using internet of things (IoT). Procedia Eng. 2015, 118, 489–497. [Google Scholar] [CrossRef] [Green Version]
- Tiwari, M.K.; Adamowski, J. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour. Res. 2013, 49, 6486–6507. [Google Scholar] [CrossRef]
- Seo, Y.; Kwon, S.; Choi, Y. Short-term water demand forecasting model combining variational mode decomposition and extreme learning machine. Hydrology 2018, 5, 54. [Google Scholar] [CrossRef] [Green Version]
- da Coelho Costa, B. Energy Efficiency of Water Supply Systems Using Optimisation Techniques and Micro-Hydroturbines. Ph.D. Thesis, Universidade de Aveiro, Aveiro, Portugal, 2016. [Google Scholar]
- Hutton, C.J.; Kapelan, Z. A probabilistic methodology for quantifying, diagnosing and reducing model structural and predictive errors in short term water demand forecasting. Environ. Model. Softw. 2015, 66, 87–97. [Google Scholar] [CrossRef]
- Yu, M.-J.; Gu, J.-Y.; Gu, Y.-H.; Kim, S.-G. Forecasting hourly water demand using linear and non-linear model. J. Korean Soc. Environ. Eng. 2004, 26, 277–283. [Google Scholar]
- Gargano, R.; Tricarico, C.; Granata, F.; Santopietro, S.; De Marinis, G. Probabilistic Models for the Peak Residential Water Demand. Water 2017, 9, 417. [Google Scholar] [CrossRef] [Green Version]
- Kofinas, D.; Mellios, N.; Papageorgiou, E.; Laspidou, C. Urban water demand forecasting for the island of Skiathos. Procedia Eng. 2014, 89, 1023–1030. [Google Scholar] [CrossRef] [Green Version]
- Zhou, S.L.; McMahon, T.A.; Walton, A.; Lewis, J. Forecasting daily urban water demand: A case study of Melbourne. J. Hydrol. 2000, 236, 153–164. [Google Scholar] [CrossRef]
- Wong, J.S.; Zhang, Q.; Chen, Y.D. Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast. Water Resour. Res. 2010, 46, W03506. [Google Scholar] [CrossRef]
- Do, N.C.; Simpson, A.R.; Deuerlein, J.W.; Piller, O. Particle filter–based model for online estimation of demand multipliers in water distribution systems under uncertainty. J. Water Resour. Plan. Manag. 2017, 143, 04017065. [Google Scholar] [CrossRef] [Green Version]
- Arandia, E.; Ba, A.; Eck, B.; McKenna, S. Tailoring seasonal time series models to forecast short-term water demand. J. Water Resour. Plan. Manag. 2016, 142, 04015067. [Google Scholar] [CrossRef] [Green Version]
- Bakker, M.; Vreeburg, J.; Van Schagen, K.; Rietveld, L. A fully adaptive forecasting model for short-term drinking water demand. Environ. Model. Softw. 2013, 48, 141–151. [Google Scholar] [CrossRef]
- Braun, M.; Bernard, T.; Piller, O.; Sedehizade, F. 24-hours demand forecasting based on SARIMA and support vector machines. Procedia Eng. 2014, 89, 926–933. [Google Scholar] [CrossRef]
- Mouatadid, S.; Adamowski, J. Using extreme learning machines for short-term urban water demand forecasting. Urban Water J. 2017, 14, 630–638. [Google Scholar] [CrossRef]
- Quevedo, J.; Saludes, J.; Puig, V.; Blanch, J. Short-term demand forecasting for real-time operational control of the Barcelona water transport network. In Proceedings of the 2014 22nd Mediterranean Conference on Control and Automation, Palermo, Italy, 16–19 June 2014; pp. 990–995. [Google Scholar]
- Chang, M.; Liu, J. Water demand prediction model based on radial basis function neural network. In Proceedings of the 2009 First International Conference on Information Science and Engineering, Nanjing, China, 26–28 December 2009; pp. 5295–5298. [Google Scholar]
- Brentan, B.M.; Luvizotto, E., Jr.; Herrera, M.; Izquierdo, J.; Pérez-García, R. Hybrid regression model for near real-time urban water demand forecasting. J. Comput. Appl. Math. 2017, 309, 532–541. [Google Scholar] [CrossRef]
- Candelieri, A. Clustering and support vector regression for water demand forecasting and anomaly detection. Water 2017, 9, 224. [Google Scholar] [CrossRef]
- Banjac, G.; Vašak, M.; Baotić, M. Adaptable urban water demand prediction system. Water Sci. Technol. Water Supply 2015, 15, 958–964. [Google Scholar] [CrossRef]
- Bougadis, J.; Adamowski, K.; Diduch, R. Short-term municipal water demand forecasting. Hydrol. Process. Int. J. 2005, 19, 137–148. [Google Scholar] [CrossRef]
- Cutore, P.; Campisano, A.; Kapelan, Z.; Modica, C.; Savic, D. Probabilistic prediction of urban water consumption using the SCEM-UA algorithm. Urban Water J. 2008, 5, 125–132. [Google Scholar] [CrossRef]
- Maier, H.R.; Jain, A.; Dandy, G.C.; Sudheer, K.P. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ. Model. Softw. 2010, 25, 891–909. [Google Scholar] [CrossRef]
- Romano, M.; Kapelan, Z. Adaptive water demand forecasting for near real-time management of smart water distribution systems. Environ. Model. Softw. 2014, 60, 265–276. [Google Scholar] [CrossRef] [Green Version]
- Adamowski, J.F. Peak daily water demand forecast modeling using artificial neural networks. J. Water Resour. Plan. Manag. 2008, 134, 119–128. [Google Scholar] [CrossRef] [Green Version]
- Herrera, M.; Torgo, L.; Izquierdo, J.; Pérez-García, R. Predictive models for forecasting hourly urban water demand. J. Hydrol. 2010, 387, 141–150. [Google Scholar] [CrossRef]
- Rangel, H.R.; Puig, V.; Farias, R.L.; Flores, J.J. Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks. J. Hydroinform. 2017, 19, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Cheifetz, N.; Noumir, Z.; Samé, A.; Sandraz, A.-C.; Féliers, C.; Heim, V. Modeling and clustering water demand patterns from real-world smart meter data. Drink. Water Eng. Sci. Discuss. 2017, 10, 75–82. [Google Scholar] [CrossRef] [Green Version]
- Lopez Farias, R.; Puig, V.; Rodriguez Rangel, H.; Flores, J.J. Multi-model prediction for demand forecast in water distribution networks. Energies 2018, 11, 660. [Google Scholar] [CrossRef] [Green Version]
- Vijai, P.; Sivakumar, P.B. Performance comparison of techniques for water demand forecasting. Procedia Comput. Sci. 2018, 143, 258–266. [Google Scholar] [CrossRef]
- Xenochristou, M.; Kapelan, Z. An ensemble stacked model with bias correction for improved water demand forecasting. Urban Water J. 2020, 17, 212–223. [Google Scholar] [CrossRef]
- Bouktif, S.; Fiaz, A.; Ouni, A.; Serhani, M.A. Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 2018, 11, 1636. [Google Scholar] [CrossRef] [Green Version]
- Kong, W.; Dong, Z.Y.; Jia, Y.; Hill, D.J.; Xu, Y.; Zhang, Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 2017, 10, 841–851. [Google Scholar] [CrossRef]
- Wang, Y.; Gan, D.; Sun, M.; Zhang, N.; Lu, Z.; Kang, C. Probabilistic individual load forecasting using pinball loss guided LSTM. Appl. Energy 2019, 235, 10–20. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Cao, H. Prediction for tourism flow based on LSTM neural network. Procedia Comput. Sci. 2018, 129, 277–283. [Google Scholar] [CrossRef]
- Pan, B.; Yuan, D.; Sun, W.; Liang, C.; Li, D. A Novel LSTM-Based Daily Airline Demand Forecasting Method Using Vertical and Horizontal Time Series. In Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, VIC, Australia, 3–6 June 2018; pp. 168–173. [Google Scholar]
- Bandara, K.; Shi, P.; Bergmeir, C.; Hewamalage, H.; Tran, Q.; Seaman, B. Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In Proceedings of the 2019 International Conference on Neural Information Processing, Sydney, NSW, Australia, 12–15 December 2019; pp. 462–474. [Google Scholar]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef] [PubMed]
- Grubbs, F.E. Procedures for detecting outlying observations in samples. Technometrics 1969, 11, 1–21. [Google Scholar] [CrossRef]
- Rahman, S.A.; Huang, Y.; Claassen, J.; Heintzman, N.; Kleinberg, S. Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data. J. Biomed. Inform. 2015, 58, 198–207. [Google Scholar] [CrossRef] [Green Version]
- Box, G.E.; Jenkins, G.M.; Reinsel, G.C. Time Series Analysis: Forecasting and Control; John Wiley & Sons: Hoboken, NJ, USA, 2015; Volume 734. [Google Scholar]
- Chen, P.; Yuan, H.; Shu, X. Forecasting crime using the arima model. In Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China, 18–20 October 2008; pp. 627–630. [Google Scholar]
- Lee, C.-M.; Ko, C.-N. Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing 2009, 73, 449–460. [Google Scholar] [CrossRef]
- Mirbagheri, S.A.; Bagheri, M.; Boudaghpour, S.; Ehteshami, M.; Bagheri, Z. Performance evaluation and modeling of a submerged membrane bioreactor treating combined municipal and industrial wastewater using radial basis function artificial neural networks. J. Environ. Health Sci. Eng. 2015, 13, 17. [Google Scholar] [CrossRef] [Green Version]
- López Frías, R.; Puig Cayuela, V.; Rodríguez Rangel, H. An implementation of a multi-model predictor based on the qualitative and quantitative decomposition of the time-series. In Proceedings of the 2015 First International work-conference on Time Series, Granada, Spain, 1–3 July 2015; pp. 912–923. [Google Scholar]
- Kantz, H.; Schreiber, T. Nonlinear Time Series Analysis; Cambridge University Press: Cambridge, UK, 2004; Volume 7. [Google Scholar]
- Werbos, P.J. Backpropagation through time: What it does and how to do it. Proceedings of the IEEE 1990, 78, 1550–1560. [Google Scholar] [CrossRef] [Green Version]
- Hochreiter, S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 1998, 6, 107–116. [Google Scholar] [CrossRef] [Green Version]
- Bengio, Y.; Simard, P.; Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 1994, 5, 157–166. [Google Scholar] [CrossRef]
- Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv 2014, arXiv:1412.6980. [Google Scholar]
- Reimers, N.; Gurevych, I. Optimal hyperparameters for deep lstm-networks for sequence labeling tasks. arXiv 2017, arXiv:1707.06799. [Google Scholar]
- Hyndman, R.J.; Koehler, A.B. Another look at measures of forecast accuracy. Int. J. Forecast. 2006, 22, 679–688. [Google Scholar] [CrossRef] [Green Version]
- McCuen, R.H.; Knight, Z.; Cutter, A.G. Evaluation of the Nash–Sutcliffe efficiency index. J. Hydrol. Eng. 2006, 11, 597–602. [Google Scholar] [CrossRef]
- Benesty, J.; Chen, J.; Huang, Y.; Cohen, I. Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin/Heidelberg, Germany, 2009; pp. 1–4. [Google Scholar]
- Creaco, E.; Blokker, M.; Buchberger, S. Models for generating household water demand pulses: Literature review and comparison. J. Water Resour. Plan. Manag. 2017, 143, 04017013. [Google Scholar] [CrossRef]
AMI No. | Diameter (mm) | Missing Rate (%) | Types | TWD (m3/Day) |
---|---|---|---|---|
110012984 | 32 | 1.438 | Restaurant | 12.206 |
110013004 | 15 | 1.610 | Domestic | 1.207 |
110013012 | 15 | 1.826 | Domestic | 1.052 |
110013044 | 25 | 1.267 | Church | 3.941 |
110013074 | 25 | 1.027 | Laundry | 1.793 |
110013629 | 25 | 1.062 | Mart | 0.950 |
110016389 | 15 | 7.957 | Pre-primary | 0.634 |
110016799 | 15 | 1.062 | Restaurant | 2.578 |
110016860 | 32 | 1.062 | Senior-citizen center | 0.636 |
110018932 | 15 | 1.096 | Domestic | 1.701 |
AMI No. | Observed | Residual (m3/day) | |||
---|---|---|---|---|---|
(m3/day) | ARIMA | RBF-ANN | QMMP+ | LSTM | |
110012984 | 12.206 | 3.276 | −0.072 | −2.103 | −0.178 |
110013004 | 1.207 | 0.517 | 0.413 | 0.248 | 0.144 |
110013012 | 1.052 | −0.528 | −0.264 | −0.086 | −0.235 |
110013044 | 3.941 | −0.360 | −1.054 | −1.369 | −0.415 |
110013074 | 1.793 | 0.840 | −0.280 | 0.464 | −0.087 |
110013629 | 0.950 | 0.430 | −0.028 | −0.053 | 0.168 |
110016389 | 0.634 | −0.363 | −0.114 | −0.241 | −0.020 |
110016799 | 2.578 | −0.164 | −0.389 | 0.791 | −0.204 |
110016860 | 0.636 | 0.241 | 0.171 | −0.035 | 0.075 |
110018932 | 1.701 | −0.566 | 0.482 | −0.161 | −0.052 |
AMI No. | RMSE (m3/h) | |||
---|---|---|---|---|
ARIMA | RBF-ANN | QMMP+ | LSTM | |
110012984 | 0.216 | 0.145 | 0.142 | 0.139 |
110013004 | 0.037 | 0.031 | 0.033 | 0.025 |
110013012 | 0.040 | 0.031 | 0.030 | 0.024 |
110013044 | 0.105 | 0.117 | 0.110 | 0.079 |
110013074 | 0.102 | 0.075 | 0.083 | 0.060 |
110013629 | 0.045 | 0.035 | 0.030 | 0.028 |
110016389 | 0.024 | 0.016 | 0.020 | 0.011 |
110016799 | 0.077 | 0.086 | 0.090 | 0.058 |
110016860 | 0.019 | 0.015 | 0.015 | 0.012 |
110018932 | 0.038 | 0.037 | 0.031 | 0.022 |
AMI No. | NRMSE (%) | |||
---|---|---|---|---|
ARIMA | RBF-ANN | QMMP+ | LSTM | |
110012984 | 27.68 | 18.56 | 18.18 | 17.78 |
110013004 | 27.92 | 23.7 | 25.08 | 19.00 |
110013012 | 29.75 | 23.34 | 22.37 | 17.93 |
110013044 | 26.8 | 29.8 | 27.9 | 20.21 |
110013074 | 23.32 | 17.08 | 18.99 | 13.80 |
110013629 | 24.42 | 19.28 | 16.62 | 15.13 |
110016389 | 38.21 | 25.09 | 31.23 | 17.04 |
110016799 | 26.43 | 29.52 | 31.05 | 20.05 |
110016860 | 23.59 | 18.99 | 18.39 | 14.95 |
110018932 | 27.72 | 27.17 | 22.7 | 15.94 |
Avg. | 27.03 | 23.32 | 23.25 | 17.92 |
AMI No. | NSE | |||
---|---|---|---|---|
ARIMA | RBF-ANN | QMMP+ | LSTM | |
110012984 | −0.12 | 0.50 | 0.52 | 0.54 |
110013004 | −0.38 | 0.00 | −0.12 | 0.36 |
110013012 | −0.43 | 0.12 | 0.19 | 0.48 |
110013044 | −0.01 | −0.25 | −0.09 | 0.43 |
110013074 | −0.05 | 0.43 | 0.30 | 0.63 |
110013629 | −0.10 | 0.32 | 0.49 | 0.58 |
110016389 | −0.65 | 0.29 | −0.10 | 0.67 |
110016799 | 0.18 | −0.02 | −0.13 | 0.53 |
110016860 | 0.24 | 0.51 | 0.54 | 0.69 |
110018932 | −0.61 | −0.54 | −0.08 | 0.47 |
Avg. | 0.11 | 0.34 | 0.34 | 0.61 |
AMI No. | PCC | |||
---|---|---|---|---|
ARIMA | RBF-ANN | QMMP+ | LSTM | |
110012984 | 0.73 | 0.73 | 0.85 | 0.73 |
110013004 | 0.37 | 0.67 | 0.56 | 0.64 |
110013012 | 0.17 | 0.66 | 0.47 | 0.84 |
110013044 | 0.23 | 0.02 | 0.45 | 0.67 |
110013074 | 0.31 | 0.71 | 0.63 | 0.81 |
110013629 | 0.34 | 0.65 | 0.71 | 0.78 |
110016389 | 0.13 | 0.60 | 0.55 | 0.83 |
110016799 | 0.64 | 0.15 | 0.63 | 0.85 |
110016860 | 0.67 | 0.80 | 0.89 | 0.85 |
110018932 | 0.08 | 0.05 | 0.57 | 0.70 |
Avg. | 0.37 | 0.64 | 0.68 | 0.79 |
Indices | ARIMA | RBF-ANN | QMMP+ | LSTM |
---|---|---|---|---|
Residual (m3/day) | 324.6 | 395.5 | 499.1 | 167.1 |
RMSE(m3/day) | 85.18 | 73.47 | 73.25 | 56.46 |
NRMSE (%) | 27.03 | 23.32 | 23.25 | 17.92 |
NSE | 0.11 | 0.34 | 0.34 | 0.61 |
PCC | 0.37 | 0.64 | 0.68 | 0.79 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Koo, K.-M.; Han, K.-H.; Jun, K.-S.; Lee, G.; Kim, J.-S.; Yum, K.-T. Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea. Sustainability 2021, 13, 6056. https://doi.org/10.3390/su13116056
Koo K-M, Han K-H, Jun K-S, Lee G, Kim J-S, Yum K-T. Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea. Sustainability. 2021; 13(11):6056. https://doi.org/10.3390/su13116056
Chicago/Turabian StyleKoo, Kang-Min, Kuk-Heon Han, Kyung-Soo Jun, Gyumin Lee, Jung-Sik Kim, and Kyung-Taek Yum. 2021. "Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea" Sustainability 13, no. 11: 6056. https://doi.org/10.3390/su13116056