The Abdi-Basid Courses Institute is a fully virtual platform aiming to unveil the engine of science and make it accessible to all. We offer online courses, live sessions, and multimedia content across fields like statistics, climate science, AI, and more. Our mission is to bridge science and society through inclusive, flexible, and engaging learning for students, professionals, educators, and curious minds worldwide.
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| The Abdi-Basid Courses Institute |
Description
This script illustrates a comprehensive approach to evaluating hydrological data (here, EgaEnEstellaQts, a series of streamflow measurements on the Ega River at the Estella station in Spain) through different methods. First, it applies a ROC (Receiver Operating Characteristic) analysis to test the discriminative capacity of the predictions. It then implements performance measures (goodness-of-fit) to compare observations and simulations, supported by graphical visualizations such as adjustment curves, scatter plots, and time series. Finally, the code provides descriptive statistics and an analysis of the data distribution. The purpose of this script is to combine statistical and graphical tools in order to assess the quality of the simulations, detect potential biases, and provide a comprehensive view of model performance when applied to environmental or hydrological datasets.
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This analysis evaluates the performance of various precipitation and climate models (CHIRPS, PERSIANN, TAMSATV3, ARCV2, ERA5) against ground-based observations using Taylor diagrams in R. Taylor diagrams provide a concise visualization of model performance by comparing correlation, standard deviation, and centered root-mean-square error (RMSE). The analysis uses synthetic data representing annual and seasonal (JF, MAM, JJAS, OND) precipitation measurements. By leveraging the plotrix and openair packages, we assess the models’ ability to replicate observed precipitation patterns, offering insights into their accuracy and reliability for climate studies.
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This R script report harnesses R’s advanced plotting libraries to explore built-in datasets (iris, mtcars, Titanic, volcano) through diverse visualization techniques, including scatter plots, boxplots, histograms, 3D scatter plots, and interactive visualizations. The code uncovers critical patterns, such as species clustering in iris or performance correlations in mtcars, making it a powerful tool for data analysis. By leveraging ggplot2, plotly, it produces publication-ready HTML output, ideal for data scientists, analysts, and educators aiming to communicate complex insights with clarity and interactivity.
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| The Abdi-Basid Courses Institute |
Project Objective
This module introduces the application of stable water isotopes (未¹⁸O and 未D) 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.
未¹⁸O (delta-O-18): The ratio of oxygen-18 (¹⁸O) to oxygen-16 (¹⁶O) in water. 未D (未²H, delta-Deuterium): The ratio of hydrogen-2 (²H, deuterium) to hydrogen-1 (¹H) in water.
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| The Abdi-Basid Courses Institute |
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.
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| The Abdi-Basid Courses Institute |
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.
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| The Abdi-Basid Courses Institute |
