About this Research Topic
Flood forecasting is a critical aspect of disaster management, as floods pose significant threats to lives, property, and the environment. In recent years, integrating artificial intelligence (AI) techniques has emerged as a promising approach to enhance the accuracy and efficiency of flood forecasting systems. AI offers the capability to process vast amounts of data, such as weather observations, hydrological data, and remote sensing imagery, to extract patterns and relationships that can improve flood inundation prediction models. Machine learning algorithms, including deep learning, neural networks, and decision trees, have been employed to create predictive models capable of providing early warnings and timely flood alerts. Moreover, AI-driven data analytics enable real-time monitoring and adaptive forecasting, making it possible to respond proactively to changing flood conditions.
In the context of flood forecasting, the challenge lies in developing AI models that can handle the complex interactions of various environmental factors, meteorological conditions, and hydrological
processes. Additionally, the incorporation of real-time data and the interpretation of uncertain inputs pose significant hurdles in generating accurate flood predictions. Therefore, it is essential to
explore and implement methodologies that overcome these obstacles and maximize the potential of AI in flood inundation as well as river flow discharge forecasting.
Potential Solutions and Recent Advances:
Hybrid Models: Integrating physics-based models with AI algorithms can combine the strengths of both approaches, improving the accuracy of flood forecasts by incorporating domain-specific knowledge.
Data Fusion: Effectively integrating heterogeneous data sources, such as weather data, satellite imagery, and river gauge measurements, can enhance the richness and completeness of input data for AI models.
Uncertainty Quantification: Implementing techniques to quantify and manage uncertainty in AI predictions can provide decision-makers with a clearer understanding of the reliability of forecasted outcomes.
Real-time Monitoring: Leveraging AI for real-time data processing and continuous monitoring allows for adaptive flood forecasting, enabling timely updates and responses to changing conditions.
Scope of the Research Topic:
The Research Topic, "Recent Progress of Artificial Intelligence Application in Flood Forecasting (flood inundation and
discharge simulations)"; aims to explore the advancements, challenges, and potential solutions related to the integration of artificial intelligence techniques in flood forecasting systems. Contributors are encouraged to address the following specific themes:
1. Novel AI Algorithms: Investigate and propose innovative machine learning and deep learning algorithms tailored for flood forecasting applications, emphasizing their accuracy and efficiency.
2. Data Integration: Explore methodologies to effectively assimilate diverse data sources, including meteorological, hydrological, and remote sensing data, to improve the reliability of flood predictions.
3. Uncertainty and Risk Assessment: Develop approaches to quantify and manage uncertainties in AI- driven flood forecasts, providing decision-makers with reliable risk assessment tools.
4. Real-time Forecasting Systems: Present cutting-edge technologies that enable real-time data processing and adaptive flood forecasting for timely and informed decision-making.
5. Case Studies and Applications: Showcase successful implementations of AI in flood forecasting in different geographic regions, highlighting practical outcomes and challenges encountered.
Types of Manuscripts of Interest:
We welcome original research articles, reviews, methodological papers, and case studies that contribute to the understanding and advancement of artificial intelligence in flood forecasting. Manuscripts should present empirical evidence, propose novel methodologies, and offer insights into the practical applications of AI in real-world flood prediction scenarios. Additionally, interdisciplinary studies that bridge the gap between AI, hydrology, meteorology, and environmental
science are encouraged. Ultimately, this Research Topic seeks to foster knowledge exchange and inspire advancements in flood forecasting through the intelligent application of AI technologies.
In the context of flood forecasting, the challenge lies in developing AI models that can handle the complex interactions of various environmental factors, meteorological conditions, and hydrological
processes. Additionally, the incorporation of real-time data and the interpretation of uncertain inputs pose significant hurdles in generating accurate flood predictions. Therefore, it is essential to
explore and implement methodologies that overcome these obstacles and maximize the potential of AI in flood inundation as well as river flow discharge forecasting.
Potential Solutions and Recent Advances:
Hybrid Models: Integrating physics-based models with AI algorithms can combine the strengths of both approaches, improving the accuracy of flood forecasts by incorporating domain-specific knowledge.
Data Fusion: Effectively integrating heterogeneous data sources, such as weather data, satellite imagery, and river gauge measurements, can enhance the richness and completeness of input data for AI models.
Uncertainty Quantification: Implementing techniques to quantify and manage uncertainty in AI predictions can provide decision-makers with a clearer understanding of the reliability of forecasted outcomes.
Real-time Monitoring: Leveraging AI for real-time data processing and continuous monitoring allows for adaptive flood forecasting, enabling timely updates and responses to changing conditions.
Scope of the Research Topic:
The Research Topic, "Recent Progress of Artificial Intelligence Application in Flood Forecasting (flood inundation and
discharge simulations)"; aims to explore the advancements, challenges, and potential solutions related to the integration of artificial intelligence techniques in flood forecasting systems. Contributors are encouraged to address the following specific themes:
1. Novel AI Algorithms: Investigate and propose innovative machine learning and deep learning algorithms tailored for flood forecasting applications, emphasizing their accuracy and efficiency.
2. Data Integration: Explore methodologies to effectively assimilate diverse data sources, including meteorological, hydrological, and remote sensing data, to improve the reliability of flood predictions.
3. Uncertainty and Risk Assessment: Develop approaches to quantify and manage uncertainties in AI- driven flood forecasts, providing decision-makers with reliable risk assessment tools.
4. Real-time Forecasting Systems: Present cutting-edge technologies that enable real-time data processing and adaptive flood forecasting for timely and informed decision-making.
5. Case Studies and Applications: Showcase successful implementations of AI in flood forecasting in different geographic regions, highlighting practical outcomes and challenges encountered.
Types of Manuscripts of Interest:
We welcome original research articles, reviews, methodological papers, and case studies that contribute to the understanding and advancement of artificial intelligence in flood forecasting. Manuscripts should present empirical evidence, propose novel methodologies, and offer insights into the practical applications of AI in real-world flood prediction scenarios. Additionally, interdisciplinary studies that bridge the gap between AI, hydrology, meteorology, and environmental
science are encouraged. Ultimately, this Research Topic seeks to foster knowledge exchange and inspire advancements in flood forecasting through the intelligent application of AI technologies.
Keywords: Artificial Intelligence (AI), Deep Learning, Flood Early Warning System, Flood forecasting, Geological Information System (GIS), Hydrology, Machine Learning (ML), Remote Sensing, Water Resource 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.
