Author/Authors :
Saudi, Ahmad Shakir Mohd Open University Malaysia - Faculty of Science and Technology, Malaysia , Saudi, Ahmad Shakir Mohd Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Juahir, Hafizan Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Azid, Azman Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Kamarudin, Mohd Khairul Amri Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Kasim, Mohd Fadhil Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Toriman, Mohd Ekhwan Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Abdul Aziz, Nor Azlina Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Che Hasnam, Che Noraini Universiti Sultan Zainal Abidin, Gong Badak Campus - East Coast Environmental Research Institute (ESERI), Malaysia , Samsudin, Mohd Saiful Universiti Putra Malaysia - Faculty of Environmental Studies, Environmental Forensics Research Center (ENFORCE), Malaysia
Abstract :
Integrated Chemometric and Artificial Neural Network were being applied in this study to identify themain contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with 0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.
Keywords :
Integrated chemometric , artificial neural network , factor analysis , time series analysis
