Amphibian occurrence prediction around water reservoirs: A machine learning perspective

Amphibians play a crucial role in ecosystem stability; their presence or absence can serve as an indicator of environmental changes. This study investigates the application of machine learning (ML) models to predict amphibian species’ occurrence, specifically the green frog, near water reservoirs. Data from satellite imagery and geographic information system (GIS) were analyzed to develop predictive models. Two classification models, Radial Basis Function (RBF) and Neural Network (NN)were evaluated alongside combination methods, including Bagging, Random Subspace Method (RSM), Boosting, and Feature-Based Combiner (FBC). Results revealed that the Neural Network combined with Boosting achieved the highest accuracy, with a classification rate nearly 70%, outperforming the RBF classifier in all combinations. The results highlight the effectiveness of combining classifiers techniques to enhance prediction accuracy and stability. These findings provide valuable insights into the potential of machine learning techniques for amphibian monitoring and ecological assessment.