About this Research Topic
Forecast and analysis play pivotal roles in environment and economics management, providing theoretical and practical supports for decision analysis and policy making. However, the arrival of the big data era places new challenges of designing effective forecasting techniques and obtaining reliable analytic results in this area. Traditional methods show very limited ability to handle big data that are of big volume, fast changing with uncertainties. Thanks to the development of artificial intelligence (AI) in recent years, showing remarkable superiority over traditional methods in some fields. It has been an inevitable trend to apply AI technologies to solve new challenges in a broad range of fields. Especially, the era of multi-modal big data shows great potentials for innovative scientific research and new real-world applications of AI. Therefore, the research and applications of AI-based intelligent forecasting and analysis for environmental and economic management is becoming a fast-growing and promising research area.
This Research Topic aims to attract researchers with an interest in the research areas described above to present the leading advances in forecast and analysis in the field of environmental and economic management and to provide new ideas for future research. Specifically, the Research Topic covers forecast and analysis issues in most environmental sectors, such as challenges related to atmospheric, rivers, lakes and seas and other environmental types. Special attention should be paid to advanced artificial intelligence and multi-modal big data mining technologies, such as neural networks and deep learning, machine learning, natural language processing, fuzzy theory, transfer learning techniques, optimization methods, data preprocessing, text mining techniques, concept drift methods, social networks analysis, sentiment analysis and so on. Moreover, forecasting and analysis research that could also address the influence of public emergencies on environmental and economic management, such as the worldwide COVID-19 pandemic, would be great additions to this Research Topic. In summary, we are interested in a large spectrum of manuscripts that can bridge existing research gaps and provide novel ideas for future research in the field, in all types of submissions including original research papers, applied research case studies, and literature reviews.
Topics of interest include, but are not limited to:
1. The environmental science data to be forecasted and analyzed:
- Atmospheric forecast and analysis
- Rivers, lakes and seas forecast and analysis
- Other ecology forecast and analysis
2. Environmental economics and management forecasting with different horizons:
- Short-term forecasting
- Mid-term forecasting
- Long-term forecasting
3. Environmental economics and management analysis from different perspectives:
- The past conditions
- The current conditions
- The future conditions
4. Methods and approaches of environment forecasting and analysis:
- Data preprocessing
- Data and text mining
- Intelligence optimization
- Artificial neural networks
- Feature selection
- Evaluation methods
- Sentiment analysis
- Econometric models
- Deep learning
- Fuzzy theory
- Machine learning
- Natural language processing
- Transfer learning
- Concept drift detection and adaptation
- Social networks analysis
- Hybrid, combined and ensemble models
- Point and interval forecast
This Research Topic aims to attract researchers with an interest in the research areas described above to present the leading advances in forecast and analysis in the field of environmental and economic management and to provide new ideas for future research. Specifically, the Research Topic covers forecast and analysis issues in most environmental sectors, such as challenges related to atmospheric, rivers, lakes and seas and other environmental types. Special attention should be paid to advanced artificial intelligence and multi-modal big data mining technologies, such as neural networks and deep learning, machine learning, natural language processing, fuzzy theory, transfer learning techniques, optimization methods, data preprocessing, text mining techniques, concept drift methods, social networks analysis, sentiment analysis and so on. Moreover, forecasting and analysis research that could also address the influence of public emergencies on environmental and economic management, such as the worldwide COVID-19 pandemic, would be great additions to this Research Topic. In summary, we are interested in a large spectrum of manuscripts that can bridge existing research gaps and provide novel ideas for future research in the field, in all types of submissions including original research papers, applied research case studies, and literature reviews.
Topics of interest include, but are not limited to:
1. The environmental science data to be forecasted and analyzed:
- Atmospheric forecast and analysis
- Rivers, lakes and seas forecast and analysis
- Other ecology forecast and analysis
2. Environmental economics and management forecasting with different horizons:
- Short-term forecasting
- Mid-term forecasting
- Long-term forecasting
3. Environmental economics and management analysis from different perspectives:
- The past conditions
- The current conditions
- The future conditions
4. Methods and approaches of environment forecasting and analysis:
- Data preprocessing
- Data and text mining
- Intelligence optimization
- Artificial neural networks
- Feature selection
- Evaluation methods
- Sentiment analysis
- Econometric models
- Deep learning
- Fuzzy theory
- Machine learning
- Natural language processing
- Transfer learning
- Concept drift detection and adaptation
- Social networks analysis
- Hybrid, combined and ensemble models
- Point and interval forecast
Keywords: big data, artificial intelligence, machine learning, intelligent forecast and analysis, environmental forecast, economic forecast, forecast and management
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
