No abstract

A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and  compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components .The results showed a  more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality.

<p>Identifying the local time scale of the torrential rainfall pattern through Singular Spectrum Analysis (SSA) is useful to separate the trend and noise components. However, SSA poses two main issues which are torrential rainfall time series data have coinciding singular values and the leading components from eigenvector obtained from the decomposing time series matrix are usually assesed by graphical inference lacking in a specific statistical measure. In consequences to both issues, the extracted trend from SSA tended to flatten out and did not show any distinct pattern.  This problem was approached in two ways. First, an Iterative Oblique SSA (Iterative O-SSA) was presented to make adjustment to the singular values data. Second, a measure was introduced to group the decomposed eigenvector based on Robust Sparse K-means (RSK-Means). As the results, the extracted trend using modification of SSA appeared to fit the original time series and looked more flexible compared to SSA.</p>

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.

In this paper, a system dynamics approach is used instead of the traditional approaches to stimulate, forecast and analyze the economic effects of an existing policy practice in Setiu Wetland. As a part of Setiu district that uphold tradition in fishery and maritime based industry, Setiu Wetland area seems to be left behind in terms of economic and livelihood. Generally, Setiu development policy consists of five subsystem including population growth, economic, residential, transportation and suburban sprawl. Due to their widespread population distribution, Setiu Wetland receives low urban-related progress. Hence, a forecast of 30 years from 2016 to 2046 providing a necessary insight for potential development of the Setiu Wetland region, to simulate its environment, identify gaps, propose suitable land model towards Setiu Minapolitan area (Peri-urban area) and suggest directions for future studies particularly in economic and livelihood for local authorities to develop with.