Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia

Abstract

In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a result, the importance of predicting future climate has become even direr. The statistical downscaling approach was introduced as a solution to provide high-resolution climate projections. An effective statistical downscaling scheme aimed to be developed in this study is a two-phase machine learning technique for daily rainfall projection in the east coast of Peninsular Malaysia. The proposed approaches will counter the emerging issues. First, Principal Component Analysis (PCA) based on a symmetric correlation matrix is applied in order to rectify the issue of selecting predictors for a two-phase supervised model and help reduce the dimension of the supervised model. Secondly, two-phase machine learning techniques are introduced with a predictor selection mechanism. The first phase is a classification using Support Vector Classification (SVC) that determines dry and wet days. Subsequently, regression estimates the amount of rainfall based on the frequency of wet days using Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Relevant Vector Machines (RVMs). The comparison between hybridization models’ outcomes reveals that the hybrid of SVC and RVM reproduces the most reasonable daily rainfall prediction and considers high-precipitation extremes. The hybridization model indicates an improvement in predicting climate change predictions by establishing a relationship between the predictand and predictors.