Use the neural networks in prediction of environmental processes

Forecasting environmental phenomena using neural networks has become increasingly popular due to their ability to analyze complex datasets and provide accurate predictions. In this paper, we investigate the application of neural networks in predicting environmental processes, focusing specifically on their use in forecasting greenhouse gas emissions. We examine several types of neural networks, including recurrent neural networks (RNNs) such as Simple RNN, LSTM-RNN, and stacked LTSM-RNN, to assess their effectiveness in handling raw data and projecting emissions over different spatial and temporal scales. The study demonstrates that the Simple RNN model is highly accurate in outlier prediction. Moreover, the consistency of results obtained through decision tree construction with those from component and cluster analyses is highlighted. The scientific significance of this study lies in its comprehensive exploration of neural networks' ability to forecast environmental processes, with a specific emphasis on greenhouse gas emissions prediction. The findings suggest that neural networks, particularly recurrent architectures, show promise as powerful tools for monitoring and forecasting environmental changes, offering valuable insights for governmental agencies and environmental initiatives.