Particle Swarm Optimization based Prediction and Ensemble Machine Learning Classification of Wildlife Habitats

The accurate classification of wildlife habitats is necessary as it provides necessary resources and condition of wildlife population. To improve this prediction task, the proposed research developed Machine Learning (ML) framework using the remote sensing data. The data is initially pre-processed using Adaptive Histogram Equalization (AHE) and the features such as spectral, special, and texture features are extracted. The features are then selected using Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The features are then optimised using Particle Swarm Optimization (PSO) for optimal prediction and then classified using Ensemble Machine Learning (EML) algorithms such as Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). The method showed better capabilities to classify different kinds of wildlife habitats and the values of integrating optimization methods with EML algorithms for solving ecological problems. The experimental results showed that the proposed PSO-EML algorithm achieved better classification accuracy and optimized prediction with an accuracy of 99.61%, and precision of 97.39% when compared to the existing methods Boosted Regression Tree (BRT) and Hierarchical clustering in handling high dimensions and noise features of ecological data.