Balanced Stochastic Realization Algorithm For Development Of Rainfall Model

Azhari, Fahimy (2014) Balanced Stochastic Realization Algorithm For Development Of Rainfall Model. Masters thesis, Universiti Sains Malaysia.

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Rainfall forecasting is an important component of flood warning to the public especially in tropical climate region like Malaysia. The forecasts can be used to make decisions about whether warnings of floods should be issued to the general public in advance. In this research, Balanced Stochastic Realization (BSR) subspace algorithm is used to develop a rainfall model for Malaysia. Simulation analysis is done using MATLAB software. The model is developed using a rainfall data obtained from Malaysia Meteorological Department (MMD). In this study, rainfall data in 2001 until 2005 from Kota Bharu station is used to simulate the model. The model is then used to predict rainfall of the same station in 2006 until 2010. The results reveal good model performance and accuracy. To further evaluate the model performance, Kota Bharu model is used to make forecasting of different places and different rain pattern in Malaysia. There are five main region classifications. Those are east coast area (Kuantan and Kuala Terengganu), northern area (Butterworth), western area (Ipoh), southern area (Senai) and east Malaysia (Miri and Sandakan). From analysis, the model also demonstrates high robustness when tested to different locations. The significant outcome from this study will able to assist further investigation on flood forecasting study in Malaysia, specifically, and tropical climatic region, generally.

Item Type: Thesis (Masters)
Additional Information: Accession No:875000376
Subjects: T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK7800-8360 Electronics
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis
Depositing User: Mr Mohd Fadli Abd Rahman
Date Deposited: 31 Jul 2018 02:18
Last Modified: 31 Jul 2018 02:18

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