Statistical analysis of an orographic rainfall for eight north-east region of India with special focus over Sikkim

Abstract

Autoregressive integrated moving average (ARIMA) models are used to predict the rain rate for orographic rainfall over a long period of time, from 1980 to 2018. As the orographic rainfall may cause landslides and other natural disaster issues. So, this study is very important for the analysis of rainfall prediction. In this research, statistical calculations have been done based on the rainfall data for twelve regions of India (Cherrapunji, Darjeeling, Dawki, Ghum, Itanagar, Kanchenjunga, Mizoram, Nagaland, Pakyong, Saser Kangri, Slot Kangri, and Tripura) from the eight states, i.e., Sikkim, Meghalaya, West Bengal, Ladakh (Union Territory of India), Arunachal Pradesh, Mizoram, Tripura, and Nagaland) with varying altitudes. The model's output is assessed using several error calculations. The model's performance is represented by the fit value, which is reliable for the northeast region of India with increasing altitude. The statistical dependability of the rainfall prediction is shown by the parameters. The lowest value of root mean square error (RMSE) indicates better prediction for orographic rainfall.