Rainfall and Atmospheric Temperature against the Other Climatic Factors: A Case Study from Colombo, Sri Lanka

Abstract

Climate prediction is given a high priority by many countries due to its importance in mitigation of extreme weather conditions. However, the prediction is not an easy task as the climatic parameters not only show spatial variations but also temporal variations. In addition, the climatic parameters are interrelated. To overcome these difficulties, soft computing techniques are widely used in prediction of climate variables with respect to the other variables. On the other hand, Colombo, Sri Lanka, is experiencing adverse or extreme weather conditions over the last few years. However, a climate prediction study is yet to be carried out in this tropical climatic zone. Therefore, this paper presents a study, identifying relationships between the two most impacted climate parameters (atmospheric temperature and rainfall) and other climatic parameters. Artificial neural network (ANN) models are developed to define the relationships and then to predict the atmospheric temperature as a function of other parameters including monthly rainfall, minimum and maximum relative humidity, and average wind speed. Same analysis is carried out to define the prediction model to the monthly rainfall. The best algorithm out of several other ANN algorithms is chosen for the analyses. Results revealed that the atmospheric temperature in Colombo can be presented with respect to the other climatic variables. However, the rainfall does not show a greater relationship with the other climatic parameters.