Invited Speaker

Abstract: Machine learning tools are extremely useful for the estimation and modelling of hydrological processes such as evapotranspiration (ET). In this study, reference evapotranspiration (ET0) in Labuan located in the East Malaysia was estimated using an artificial neuro-fuzzy inference system (ANFIS). In order to investigate the feasibility of the ANFIS model for a wide temporal range, daily meteorological data collected at Station 96465 (Labuan) from year 2014 to 2018 were divided on an annual basis. ANFIS models were trained using data from different years as well as varying combinations of one climatic parameter with solar radiation. The study revealed that the ANFIS model was capable of performing accurate estimation when only one year of training data were used where errors of less than 5 % and NSE above 0.950 were achieved. This finding could be useful for new meteorological stations where data are limited. Furthermore, solar radiation and minimum temperature were deemed to be the best input combination because of their distinguishable characteristics. Maximum temperature which highly overlaps solar radiation in nature was found the worst complementary input. However, it is important to note that the importance of climatic parameters could be affected by extreme weather conditions.

Biography: Ir. Dr. Huang Yuk Feng is an Associate Professor at the Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman (UTAR), Malaysia. He is also the Chairperson of the Research Centre for Disaster Risk Reduction (CDRR) at UTAR. He is a Professional Engineer with Practice Certificate of the Board of Engineers Malaysia (BEM). He has over 20 years of experience in research, and his research interests include water resources engineering, climate change, drought forecasting, evapotranspiration estimation, reservoir inflow forecasting, sedimentation, water quality, artificial intelligence modelling, rainfall-runoff analysis, and other hydrological studies.