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

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.

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

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

Abdi-Basid ADAN, 2025

The Abdi-Basid Courses Institute

Visualizing SRTM-NASA DEMs in ArcGIS: Practical Examples

🔗 Please Read it here https://abdibasidadan.medium.com

🔗 Where to watch the tutorial : https://www.youtube.com

Abdi-Basid ADAN, 2025

The Abdi-Basid Courses Institute

Multi-Source Climate Data Analysis for Cyclone Tracking, Soil Moisture Dynamics, and Land–Atmosphere Interactions (1987–2022)

🔗 Description Read it here: https://abdibasidadan.medium.com

Abdi-Basid ADAN, 2023

The Abdi-Basid Courses Institute

Temporal and Spatial Assessment of temperature and precipitation over Djibouti (1961–2017)

The study area covers the capital of the Republic of Djibouti (Lat: 11.33 and long: 43.50), located to the southwest. A province of Africa in which climate change is impacting rural populations. The work of (Ozer and Ayan, 2013) describes it. The temporal evaluation will be limited only to the capital. The station at Djibouti International Airport provides historical data on precipitation and min and max temperature. The spatial framework covers the five regions of the Republic of Djibouti, including Tadjourah, Dikhil (Lat: 11.10 and Long: 42.37), Ali-Sabieh (Lat: 11.15 and Long: 42.70), Arta (Lat: 11.50 and Long: 42.83) and Obock (Lat: 11.97 and Long: 43.28). We analyze a daily historical base of the station of the international airport of Djibouti (Lat: 11.33 and long: 43.50). For precipitation, the daily series is between 1980 and 2017. We also had another series of data from 1950 to 1970. For lack of more than 10 years of information, the latter is neglected. The monthly precipitation ranges from 1961 to 2017. With regard to temperature, the daily data series is studied between 1966 to 2009. We also have the same monthly series from 1961 to 2017.