Forecsting of Hydrological Time Series Data with Lag-one Markov Chain Model
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Keywords

Rainfall forecasting
Lag-one Markov chain
model
stochastic
synthetic rainfall data

Abstract

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.

https://doi.org/10.29037/ajstd.26
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