About this Research Topic
Water and agricultural resources are critical components of our ecosystem and economy, facing unprecedented challenges due to climate change, population growth, and increasing environmental pressures. Traditional management approaches are often inadequate to address the complex, dynamic nature of these interconnected systems. The rapid advancement of artificial intelligence (AI) and big data analytics offers promising solutions to enhance our understanding, monitoring, and management of water and agricultural resources. These technologies provide unprecedented capabilities to process vast amounts of data from diverse sources, uncover hidden patterns, and generate actionable insights. The integration of AI and big data with domain expertise in hydrology, ecology, and agriculture has the potential to revolutionize resource management practices, leading to more sustainable and resilient systems.
Recent advancements in deep learning, computer vision, and sensor technologies have opened new avenues for addressing these challenges. For instance, convolutional neural networks have shown promise in analyzing satellite imagery for crop yield prediction and land use classification. Graph neural networks are emerging as powerful tools for modeling complex water distribution networks. Additionally, physics-informed neural networks are bridging the gap between data-driven and mechanistic modeling approaches in hydrology.
We welcome Original Research papers, Review articles, and Brief Research Reports that address the following topics:
(1) Intelligent Water Ecosystem Monitoring and Multi-Model Ensemble Forecasting
• Applications of smart sensor networks in water quality and quantity monitoring
• Machine learning-based hydrological prediction models
• Multi-source data fusion techniques in water ecosystem monitoring
• Integrated multi-model water resource forecasting systems
• AI-assisted water ecosystem health assessment
(2) Wetland and Crop Growth Monitoring Based on Multi-Source Remote Sensing Platforms
• Synergistic use of satellite remote sensing, drones, and ground sensors
• Applications of hyperspectral and multispectral image analysis techniques in vegetation monitoring
• Deep learning-based remote sensing image classification and change detection
• Remote sensing methods for crop growth condition and yield prediction
• Multi-scale monitoring methods for wetland ecosystem dynamics
(3) Applications of Artificial Intelligence in River and Lake Ecosystem Monitoring, Evaluation, Prediction, and Regulation
• Computer vision applications in aquatic biodiversity assessment
• Deep learning-based inversion and prediction of water quality parameters
• Development and application of intelligent ecological flow regulation systems
• Intelligent diagnosis and early warning of river and lake ecosystem health
• AI-based optimization of aquatic ecosystem restoration schemes
(4) Water Environment Dynamic Modeling Integrating Physical Mechanisms and Machine Learning
• Applications of hybrid physical-statistical models in hydrological simulation
• Machine learning-enhanced water quality models
• Ecological system models combining data-driven and process-driven approaches
• Applications of deep learning-based parameterization schemes in water environment models
• Integrated modeling methods for multi-scale water environment processes
Recent advancements in deep learning, computer vision, and sensor technologies have opened new avenues for addressing these challenges. For instance, convolutional neural networks have shown promise in analyzing satellite imagery for crop yield prediction and land use classification. Graph neural networks are emerging as powerful tools for modeling complex water distribution networks. Additionally, physics-informed neural networks are bridging the gap between data-driven and mechanistic modeling approaches in hydrology.
We welcome Original Research papers, Review articles, and Brief Research Reports that address the following topics:
(1) Intelligent Water Ecosystem Monitoring and Multi-Model Ensemble Forecasting
• Applications of smart sensor networks in water quality and quantity monitoring
• Machine learning-based hydrological prediction models
• Multi-source data fusion techniques in water ecosystem monitoring
• Integrated multi-model water resource forecasting systems
• AI-assisted water ecosystem health assessment
(2) Wetland and Crop Growth Monitoring Based on Multi-Source Remote Sensing Platforms
• Synergistic use of satellite remote sensing, drones, and ground sensors
• Applications of hyperspectral and multispectral image analysis techniques in vegetation monitoring
• Deep learning-based remote sensing image classification and change detection
• Remote sensing methods for crop growth condition and yield prediction
• Multi-scale monitoring methods for wetland ecosystem dynamics
(3) Applications of Artificial Intelligence in River and Lake Ecosystem Monitoring, Evaluation, Prediction, and Regulation
• Computer vision applications in aquatic biodiversity assessment
• Deep learning-based inversion and prediction of water quality parameters
• Development and application of intelligent ecological flow regulation systems
• Intelligent diagnosis and early warning of river and lake ecosystem health
• AI-based optimization of aquatic ecosystem restoration schemes
(4) Water Environment Dynamic Modeling Integrating Physical Mechanisms and Machine Learning
• Applications of hybrid physical-statistical models in hydrological simulation
• Machine learning-enhanced water quality models
• Ecological system models combining data-driven and process-driven approaches
• Applications of deep learning-based parameterization schemes in water environment models
• Integrated modeling methods for multi-scale water environment processes
Keywords: Artificial intelligence, Big data, Water resource management, Agricultural resource management, Multi-source remotely sensed data fusion, Ecosystem health
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.
