Published Paper Details:

Soil erosion is a severe environmental problem that leads to the deterioration of arable land, poor agricultural production, and susceptibility to natural disasters such as floods and landslides. Early detection and continuous monitoring of soil erosion are essential for effective land management and sustainable development. In this study, we propose a machine learning-based soil erosion detection technique using the Gradient Boosting algorithm. Gradient Boosting is a powerful ensemble learning method that creates predictive models by ensembling a number of weak learners to achieve high accuracy and robustness. The proposed system integrates multisource environmental data, including satellite imagery, topographic attributes, rainfall intensity, soil texture, and vegetation indices, to train a gradient boosting classifier model to distinguish between erosion-prone and stable regions. The model's performance is evaluated in terms of accuracy, precision, recall, and F1-score measures, demonstrating the model's effectiveness in handling complex, non-linear relationships in environmental datasets.