Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

• Published in: WATER PRACTICE AND TECHNOLOGY
• Main Authors: Ng, Jing Lin; Huang, Yuk Feng; Chong, Aik Hang; Ahmed, Ali Najah; Syamsunurc, Deprizon
• Format: Article
• Language: English
• Published: IWA PUBLISHING 2024
• Subjects: Water Resources
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001342049200001
• ISSN: 1751-231X
• DOI: 10.2166/wpt.2024.265

Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input-output patterns and provide highly accurate estimation results.