Temporal Dynamics of Trend in Relative Humidity with RH-SARIMA Model

Abstract

The relative humidity (RH) of 13 stations all over the peninsular Malaysia for the period of 1968 to 2009 is examined in this study. In understanding the trend flow, the Mann-Kendall (MK) trend test of RH of selected 13 stations all over Malaysia reported a decreasing trend over all parts excluding one station. RH prediction is an important problem in the climate change study; it determines future trend based on past values. The main goal of this paper is to create a model and make future trend predictions using RH data. Among the most effective and prominent approaches for analysing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build an RH-Seasonal Autoregressive Integrated Moving Average model (SARIMA) for monthly RH data. The RH-SARIMA model for each station was developed. These models were used to forecast 30 months upcoming RH data. The result will help decision makers to establish priorities in terms of climate change impact over peninsular Malaysia.

Windstorm Hazardous Area Mapping Using Multi-Criteria Evaluation Techniques of Fuzzy Logic Approach

“…The higher sensitivity value located at high altitude area at middle of map which occupy 2.59% of area. The average relative humidity was 86% and 76% approximately [26] . The high relative humidity indicates percentage that represents the amount of water vapor in the air is high.…”