Nonlinear dynamic analysis of meteorological variables for Ha'il region, Saudi Arabia, for the period 1990-2022

The study applies diverse methods of chaos detection to meteorological variable data (air temperature, relative humidity, surface pressure, precipitation, and wind speed for Ha'il, Saudi Arabia) understand the nonlinear dynamics classify their nature. Additionally, Random Forest Algorithm model is used predict precipitation speed. results obtained by classical modern approaches are compared. All variables found be chaotic based on correlation dimension, approximate entropy, 0-1 test. decision tree algorithm diagnoses air as chaotic, while pressure identified stochastic. This shows that well-validated with methods. Nevertheless, some them contradict analysis 32 years showed no 92% time during entire period algorithm.