Open AccessEXTREME RAINFALL FORECASTING MODEL BASED ON DESCRIPTIVE INDICES
Author(s) -
Yasir Hilal Hadi,
Ku Ruhana Ku-Mahamud,
Wan Hussain Wan Ishak
Publication year - 2019
Publication title -
journal of technology and operations management/journal of technology and operations management
Language(s) - English
Resource type - Journals
eISSN - 2590-4175
pISSN - 1823-514X
DOI - 10.32890/jtom2019.14.1.4
Subject(s) - extreme value theory , extreme weather , environmental science , climatology , meteorology , extreme learning machine , artificial neural network , statistics , computer science , mathematics , geography , climate change , geology , machine learning , oceanography
Extreme rainfall is one of the disastrous events that occurred due to massive rainfall overcometime beyond the regularrainfall rate. The catastrophic effects of extreme rainfall on human, environment, and economy are enormous as most of the events are unpredictable. Modelling the extreme rainfall patterns is a challenge since the extreme rainfall patterns are infrequent.In this study, a model based on descriptive indices to forecast extreme rainfall is proposed. The indices that are calculated every monthare used to develop a Back Propagation Neural Network model in forecasting extreme rainfall. Experiments were conducted using different combinations of indices and results were compared with actual data based on mean absolute error. The results showed that the combination of six indices achieved the best performance,and this proved that indices couldbe used for forecasting extreme rainfall values.