Machine learning (ML) has emerged as a transformative tool in the 21st century, enabling humans to tackle complex problems where traditional analytical methods fail to discern input-output relationships. A prime example is weather forecasting, where ML techniques have achieved significant advancements in accuracy and reliability. As these methods mature, their potential applications continue to expand across diverse domains. This study aimed to predict alpha diversity using 25 input features through machine learning. Initially, a Deep Neural Network (DNN) model was employed, but its performance proved inadequate. To address this limitation, we investigated alternative approaches, including Transformer and Random Forest models. The dataset was rigorously pre-processed using PCA techniques to mitigate potential biases and enhance model generalizability. Our results demonstrate that the Random Forest model consistently outperformed the DNN and Transformer models under the tested conditions. However, we caution against concluding that Random Forests universally dominate, as further hyperparameter optimization could alter the comparative performance of these models.
Ning et al. (Wed,) studied this question.