Analyzing trend and forecast of rainfall and temperature in Valmiki Tiger Reserve, India, using non-parametric test and random forest machine learning algorithm

• Published in: Acta geophysica 2023-02, Vol.71 (1), p.531-552
• Main Authors: Roshani, Sajjad, Haroon, Saha, Tamal Kanti, Rahaman, Md Hibjur, Masroor, Md, Sharma, Yatendra, Pal, Swades
• Format: Article
• Language: English
• Subjects: Algorithms; Climate change; Climate variability; Climatic analysis; Earth and Environmental Science; Earth Sciences; Errors; Flora; Forecasting; Geographical locations; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Machine learning; Maximum temperatures; Nonparametric statistics; Protected areas; Rain; Rainfall; Rainfall forecasting; Research Article - Atmospheric & Space Sciences; Seasonal forecasting; Seasons; Structural Geology; Weather forecasting
• ISSN: 1895-7455; 1895-6572; 1895-7455
• DOI: 10.1007/s11600-022-00978-2

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen’s slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations.