Forecsting of Hydrological Time Series Data with Lag-one Markov Chain Model
Planning and operation are important elements in water resource management. Rainfall forecasting is one of the conducts commonly used to extend the lead-time for catchments with short response time. However, it is difficult to obtain a high degree of accuracy in rainfall forecasting using determinis...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2014-06-01
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| Series: | ASEAN Journal on Science and Technology for Development |
| Subjects: | |
| Online Access: | http://www.ajstd.org/index.php/ajstd/article/view/26 |
| Summary: | Planning and operation are important elements in water resource management. Rainfall forecasting is one of the conducts commonly used to extend the lead-time for catchments with short response time. However, it is difficult to obtain a high degree of accuracy in rainfall forecasting using deterministic models. Therefore, a probability-based rainfall forecasting model, based on Markov Chain provided a better alternative due to its ability to preserve the basic statistical properties of
the original series. This method was especially useful in the absence of long-term recorded data, a rampant phenomenon in Malaysia. Comparison of statistics in the generated synthetic rainfall data against those of the observed data revealed that reasonable levels of acceptability were achieved. |
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| ISSN: | 0217-5460 2224-9028 |