ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction
| Autor: | Hongyan Du, Huifeng Xue, Zhihua Zhao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Hydraulic engineering
Computer science Geography Planning and Development 0207 environmental engineering Scheduling (production processes) Water supply 02 engineering and technology autoregressive moving average model 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry Superposition principle lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 water resource management Statistics Autoregressive–moving-average model Autoregressive integrated moving average 020701 environmental engineering Randomness 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 sustainable development Markov chain business.industry other water consumption prediction markov chain business Error detection and correction |
| Zdroj: | Water, Vol 12, Iss 3, p 760 (2020) Water Volume 12 Issue 3 |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w12030760 |
| Popis: | Water resource is considered as a significant factor in the development of regional environment and society. Water consumption prediction can provide an important decision basis for the regional water supply scheduling optimizations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model was proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points were used to verify the effectiveness of the model, and the future water consumption was predicted in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA. |
| Databáze: | OpenAIRE |
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