Comparative Analysis of Predictor Importance for Rainfall in Climatic Data: Relative Weights Analysis (RWA), Machine Learning, and Statistical Methods

Project Objective

This code evaluates and compares the influence of various climatic variables (temperature, pressure, humidity, wind characteristics, sunshine, cloud cover, evapotranspiration, soil moisture) on rainfall. By applying Relative Weights Analysis (RWA), iopsych relative weights, relimp (relative importance in linear regression), and Random Forest variable importance, it identifies which predictors contribute most to rainfall variability. The approach provides a robust understanding of the dominant climatic drivers, allowing researchers to prioritize variables for predictive modeling and better interpret their impact on rainfall patterns. The comparison across multiple methods ensures the reliability and consistency of variable importance assessments.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : https://rpubs.com/abdibasidadan/

Abdi-Basid ADAN, 2025

The Abdi-Basid Courses Institute

Linear and Machine Learning Models for Rainfall Prediction (M5P Trees)

The objective of this code is to predict rainfall using simulated climate variables (temperature, pressure, humidity, wind) through various modeling approaches, ranging from linear and generalized regression to advanced models like M5P regression trees. The focus is on building and comparing predictive models, validating their performance using train/test splits and cross-validation, and quantitatively evaluating predictions with metrics such as RMSE and R². This code enables the exploration of model robustness, identification of the most influential variables, and visualization of model fit through plots comparing observed and predicted values, making the analysis both educational and applicable to real climate datasets.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : https://rpubs.com/abdibasidadan/

The Abdi-Basid Courses Institute

Meteorological Variable Relationships and Regression Analysis

The objective of this project is to identify relationships between various meteorological variables. This pursuit has two main goals: first, to explore correlations and the significance of relationships between climatic factors; second, to build and compare multiple linear regression models to determine the most relevant predictors of rainfall. This approach allows testing variable selection methods and performance criteria (adjusted R², MSE, AIC, Mallows’ Cp, etc.) in a controlled setting, providing a pedagogical exercise and a methodological foundation transferable to real-world climate data analysis.