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

M. A Malek (The Institute of Energy, Policy and Research, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009, Kajang, Selangor)
A.M Baki (Faculty of Civil Engineering, Universiti Technologi MARA, Shah Alam, Selangor)

ASEAN Journal on Science and Technology for Development
Vol. 31 No. 1 (2014): ASEAN Journal on Science and Technology for Development (AJSTD)

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 ofthe 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.