Recognizing Rainfall Pattern for Pakistan using Computational Intelligence

Over the world, rainfall patterns and seasons are shifting in new directions due to global warming. In the case of Pakistan, unusual rainfall events may outcome with droughts, floods and other natural disasters along with disturbance of economy, so the scientific understating of rainfall patterns will be very helpful to water management and for the economy. In this paper, we have attempted to recognize rainfall patterns over selected regions of the Pakistan. All the time series data of metrological stations are taken from the PMD (Pakistan Meteorological Department). Using PCA (Principal Component Analysis), monthly metrological observations of all the stations in Punjab have been analyzed which covers the area of 205,344 km² and includes monsoon-dominated regions. To tackle the problem of inter-annual variations, trend detection, and seasonality, rainfall data of Lahore, the Pakistan is taken that covers the period of 1976-2006. To obtain results, MASH (Moving Average over Shifting Horizon), PCA (Principal Component Analysis) along with other supporting techniques like bi-plots, the Pair-wise correlation has been applied. The results of this study successfully show seasonal patterns, variations and hidden information in complex precipitation data structure.