2025-08-20

Bayesian Analysis of Precipitation Fluxes in a Climatic Context Using MixSIAR

Project Objective

This module introduces the application of stable water isotopes (δ¹⁸O and δD) to quantify the origin of precipitation and hydrological inputs under varying climatic conditions. Using Bayesian mixing models implemented in MixSIAR (R package), participants will learn to:

Prepare and structure isotopic datasets from precipitation, potential water sources, and discrimination factors.

Apply a Bayesian mixing model to estimate the relative contributions of different hydrological sources (rainfall, snowmelt, groundwater).

Evaluate model outputs through statistical summaries and convergence diagnostics (e.g., MCMC trace plots, Gelman–Rubin statistics) to ensure robustness.

Visualize estimated source proportions and their posterior distributions, enabling clear interpretation in both climatic and hydrological studies.

By the end of the module, participants will be able to trace water fluxes within a watershed, assess the climatic influence on precipitation sources, and communicate their findings effectively using results derived from Bayesian analysis.

In this training context, simulated datasets are employed to provide order-of-magnitude examples and hands-on practice with MixSIAR.

δ¹⁸O (delta-O-18): The ratio of oxygen-18 (¹⁸O) to oxygen-16 (¹⁶O) in water. δD (δ²H, delta-Deuterium): The ratio of hydrogen-2 (²H, deuterium) to hydrogen-1 (¹H) in water.

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

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

Abdi-Basid ADAN

2025-08-20

The Abdi-Basid Courses Institute

2025-08-19

Machine Learning Approaches for Predicting Temperature using the New York AirQuality Dataset (May–September 1973)

Project Objective

The AirQuality dataset in R contains daily air quality measurements collected in New York City from May to September 1973. The dataset includes variables such as ozone concentration, solar radiation, wind speed, and daily temperature. In this analysis, we focus on predicting temperature, which is a key climatic variable with strong implications for environmental studies, health impacts, and energy demand forecasting. We apply two machine learning models: Random Forest, a powerful ensemble method that captures complex, non-linear relationships between predictors, and Neural Network (shallow), which provides an alternative regression approach by simulating interconnected neurons. By comparing the two models, we can assess their predictive performance and understand the relative importance of different meteorological variables in explaining temperature variations during this historical air quality study.

🎯 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

2025-08-17

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.