Special Issue "Application of Numerical Models and Data-Driven Intelligent Systems in Flood Forecasting"
Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 2325
Interests: hydrology; disaster mitigation; water resource management
Floods are the most cataclysmic of disasters among the natural hazards. The World Meteorological Organization has claimed that flash floods account for approximately 85% of flooding cases and also have the highest mortality rate of the natural hazards. They are among the world’s deadliest disasters with more than 5000 lives lost annually. Thus, flood disasters not only largely affect people’s lives and properties but lead to severe damage to infrastructures and economies. However, floods are also a natural outcome of rivers and are highly nonlinear in localized watershed systems. How to establish a suitable flood forecasting system for local contexts to protect people from disaster is a crucial issue.
Accompanying the great advances in computational facilities in recent years, the use of numerical approaches to implement high-resolution simulations is becoming more feasible. In addition, given the vast range of novel technologies available in the domains of sensing systems, communication networks, cloud/edge computing, machine learning, data-driven methods, etc., the above state-of-the-art techniques are readily available for application toward establishing an intelligent flood forecasting system to protect people from danger.
We look forward to receiving contributions in the form of research articles and reviews for this Special Issue. Specific topics of interest include but are not limited to the following:
- Smart Flood Forecasting System Using IoT & AI
- Comparative Studies of Very Short-Term Flood Forecasting Using Physics-Based and Data-Driven Prediction Models
- Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model
- Application of Numerical Models for Improvement of Flood Preparedness
- An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods
- Improving Operational Flood Forecasting Using Data Assimilation
- Flood Prediction Using Machine Learning Models
Prof. Dr. Ray-Shyan Wu
Dr. Dong-Sin Shih
Manuscript Submission Information
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- flood forecasting
- numerical model
- data-driven model
- Internet of Things (IoT)
- sensing systems
- cloud/edge computing
- machine learning
- early warning systems