Analyzing trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree machine learning approaches

Abstract

The changing pattern of climate variables has caused extreme weather events and severe disasters, especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine the trends and forecasts of meteorological variables using a scientific modelling approach. This study investigates temperature and rainfall trends using the modified Mann-Kendall test and Sen's slope estimator between 1980 and 2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trends for the next 30 years (2022–2051) to understand the temporal dynamics in Shimla district of the Indian Himalayan state. The mean absolute percentage error, mean absolute error, root mean squared error and correlation coefficient were determined to assess the effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased during the monsoon season (June–September) from 1980 to 2021. Significant trends were found in annual rainfall, maximum, minimum and mean temperatures while rainfall during the winter, summer and post-monsoon seasons has shown a declining trend. The forecast analysis revealed a significant trend for rainfall during the monsoon season and an increasing trend in the maximum temperature has been observed during the winter and summer seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat climate change in hilly regions. The methodology adopted in the study may help in the future progression of the research in different geographical regions for trend and climate forecasting.