Evaluation of Stochastic Daily Rainfall Data Generation Models

@inproceedings{Jaafar2016EvaluationOS,
  title={Evaluation of Stochastic Daily Rainfall Data Generation Models},
  author={J. Jaafar and A. Baki and I. Bakar and W. Tahir and H. Awang and F. Ismail},
  year={2016}
}
In developing countries, data is usually a scarce resource as data collection is an expensive exercise. Therefore, analytical method is required to simulate the actual situations and provide synthetic data for forecasting purposes. This paper will compare several methods of synthetically generating rainfall data based on available data. Several models will be used, including lag-one Markov chain model, two-step model, and transition probability model to generate stochastic daily rainfall data… Expand
Stochastic modelling of seasonal and yearly rainfalls with low-frequency variability
Stochastic rainfall models are important for many hydrological applications due to their appealing ability to simulate synthetic series that resemble the statistical characteristics of the observedExpand

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