Random Forest

SAR based flood mapping using sentinel-1 images, Golestan province, Iran

Comparison of SVM and RF Algorithms for Crop Mapping Using Bi-Temporal Optical and Radar Data with Limited Training Samples

This paper aims to compare two state of the art classification algorithms, namely Support Vector Machine SVM and Random Forest RF algorithms, in term…

Providing Classification Map of PM2.5 Air Pollution Using Decision Tree and Random Forest methods

Comparison Between XGBoost and Random Forest in Credit Risk Assessment Using the German Credit Dataset

The imperative for accurate, robust, and transparent credit risk models is a cornerstone of modern financial stability This study conducts a rigorous…

urban flood risk prediction with machine learning models

Validation and Accuracy Assessment in Landslide Susceptibility Mapping: A Machine Learning Model Comparison

Developing reliable landslide susceptibility maps LSM represents a fundamental requirement for effective hazard mitigation, particularly within rapid…

Comparative Analysis of Three Machine Learning Models for Predicting Daily CO۲ Emissions in the Residential Sector: A Future Trend Forecasting Approach

The residential sector is a primary source of greenhouse gas GHG emissions and plays a vital role in achieving emission reduction goals While most re…

Industrial Air Quality Assessment through Multi-Source Data and Random Forest Modeling: A Case Study of Assaluyeh, Iran

Air pollution is one of the most challenging environmental problems in industrial areas such as Assaluyeh, Iran In this study, three sources of terre…