Forecasting of Humidity of Some Selected Stations from the Northern Part of Bangladesh: An Application of SARIMA Model

Community risk from natural hazards and climate change depends largely on physical and climatic settings of an area, socio-economic condition of a community and the magnitude, duration and consecutiveness of the hazard or change itself. Impacts of climate change can be characterized by increasing temperatures, rainfall, humidity changes and climate related extreme events such as floods, cyclone, droughts, sea level rise, salinity and soil erosion etc. Humidity affects crops through evaporation, transpiration and condensation. Crop agriculture is highly influenced by climatic change and majority of population is dependent on agricultural crop in Bangladesh. Any unfavorable change in future climate could have a devastating impact on agriculture and the economy of the country. It is needed to know the socio-economic settings of the rural community, their agricultural practices, anticipated changes in climatic parameters and the link between the climatic variables and crop growth and productivity. Time Series analysis and forecasting has become a major tools in different applications in meteorological phenomena, such as rainfall, humidity, temperature, draught etc. and environmental management fields. Among the most effective approaches for analyzing time series data is the Autoregressive Integrated Moving Average (ARIMA) model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build seasonal ARIMA model for monthly Humidity data taken from Bogra, Dinajpur, Rajshahi and Rangpur stations over the period January, 2001 to October, 2014. In this study, ARIMA (2,0,2)(2,1,2)₁₂, ARIMA (0,1,2)(1,1,1)₁₂, ARIMA (1,0,2)(2,1,1)₁₂ and ARIMA (1,0,2)(2,1,2)₁₂ model respectively are found to be suitable models for Dinajpur, Rajshahi, Bogra and Rangpur stations respectively and these models are used to forecasting the monthly humidity for the upcoming two years to help decision makers to establish priorities in terms of water demand management.