Application of Digital Twins and Artificial Intelligence Technology in Watershed Flood Disaster Warning and Control
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 3694
Special Issue Editors
Interests: meteorological and hydrological forecasting; digital twin watershed; artificial intelligence technology; distributed hydrological model; disaster risk assessment
Interests: basin water resource management; complex system analysis and modeling; urban lake habitat restoration; digital watershed technology and application; geographic information system; development and integration of decision support systems
Special Issue Information
Dear Colleagues,
Flood disasters are one of the top ten most severe natural disasters worldwide, causing significant destruction to human society and the economy. The occurrence of flood disasters is usually accompanied by heavy rainfall, river overflow, and the failure of urban drainage systems. A severe case is the 2018 flood disaster in the Indian state of Himachal Pradesh. In this catastrophe, Himachal Pradesh experienced one of the most severe episodes of heavy rain and flooding in its history. Continuous heavy rainfall led to rapid river flooding, destroying many villages and farmland, resulting in numerous casualties and property losses. This flood disaster highlighted the deficiencies in flood warning systems, with a lack of accurate predictions and timely alerts making rescue efforts extremely challenging.
Artificial intelligence can improve the accuracy and timeliness of flood prediction and warning by analyzing large amounts of meteorological data, hydrological data, and terrain information. Additionally, digital twin technology can construct virtual models of watersheds, simulating flood propagation and impacts to provide decision-makers with more accurate disaster assessments and emergency response plans. The development of flood deduction and assessment technologies based on watershed digital twins can help us better understand the development process of flood disasters, predict the extent and impact of flooding, and formulate corresponding forecasting, warning, and simulation plans. By improving the accuracy of prediction and warning, we can take timely measures to protect lives and properties, reducing the losses caused by floods. Therefore, introducing advanced technologies such as artificial intelligence and digital twins is crucial to enhance the capacity to respond effectively to flood disasters.
The theme of this Special Issue includes but is not limited to the following topics:
(1) Research on short-term and medium-term prediction and warning techniques for extreme rainfall disasters based on artificial intelligence technology.
(2) Research on hydrological forecasting models that couple artificial intelligence with physical mechanisms.
(3) Research on meteorology-hydrology-hydraulics coupling for watershed flood risk warning techniques.
(4) Research on dynamic deduction techniques for watershed flood disasters based on digital twin technology.
(5) Research on emergency evacuation route optimization techniques for flood inundation processes.
Dr. Jun Guo
Dr. Yi Liu
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence technology
- extreme rainfall prediction
- hydrological forecasting
- meteorology-hydrology-hydraulics coupling
- watershed flood risk warning
- digital twin technology
- flood dynamic deduction
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