Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions of lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases in Malaysia as reported by the Ministry of Health Malaysia (MOHE). This study aims to evaluate the trend analysis of the COVID-19 outbreak using Mann-Kendall test, and predict the future cases of COVID-19 in Malaysia using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) model. The RF-SSA model was developed to measure and predict daily COVID-19 cases in Malaysia for the coming 10 days using previously-confirmed cases. A Singular Spectrum Analysis-based forecasting model that discriminates noise in a time series trend is introduced. The RF-SSA model assessment is based on the World Health Organization (WHO) official COVID-19 data to predict the daily confirmed cases after 29th April until 9th May, 2020. The preliminary results of Mann-Kendall test showed a declining trend pattern for new cases during Restricted Movement Order (RMO) 3 compared to RMO1, RMO2 and RMO4, with a dramatic increase in the COVID-19 outbreak during RMO1. Overall, the RF-SSA has over-forecasted the cases by 0.36%. This indicates RF-SSA’s competence to predict the impending number of COVID-19 cases. The proposed model predicted that Malaysia would hit single digit in daily confirmed cased of COVID-19 by early-June 2020. These findings have proven the capability of RF-SSA model in apprehending the trend and predict the cases of COVID-19 with high accuracy. Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes.

Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.