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 June 2024 | Viewed by 2412

Special Issue Editors


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Guest Editor
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: meteorological and hydrological forecasting; digital twin watershed; artificial intelligence technology; distributed hydrological model; disaster risk assessment

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Guest Editor
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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

Published Papers (2 papers)

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Research

18 pages, 6625 KiB  
Article
Evaluation Method of Severe Convective Precipitation Based on Dual-Polarization Radar Data
by Zhengyang Tang, Xinyu Chang, Xiu Ni, Wenjing Xiao, Huaiyuan Liu and Jun Guo
Water 2024, 16(8), 1136; https://doi.org/10.3390/w16081136 - 17 Apr 2024
Viewed by 573
Abstract
With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement [...] Read more.
With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (ZH) and differential reflectance (ZDR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model’s assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar ZH and ZDR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events. Full article
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17 pages, 4777 KiB  
Article
Evaluation of Subdaily Hydrological Regime Alteration Characteristics for Hydro–Photovoltaic Complementary Operation in the Upper Yellow River
by Guoyong Zhang, Hongbin Gu, Weiying Wang, Silong Zhang and Lianfang Xue
Water 2024, 16(2), 300; https://doi.org/10.3390/w16020300 - 16 Jan 2024
Cited by 1 | Viewed by 886
Abstract
The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index [...] Read more.
The complementary operation of hydropower and photovoltaic power, aimed at meeting real-time demand, has led to frequent adjustments in power generation, causing significant fluctuations in hydrological systems and adversely affecting fish reproduction. The traditional hydrological regime alteration assessment index is based on index of hydrologic alternation (IHA) and mostly focuses on annual and daily runoff alterations. This study proposes a new set of indicators considering the characteristics of subdaily hydrological regime alterations, including magnitude, rate of change, duration, frequency, and timing. Using the hourly outflow from Longyangxia, an analysis of indicator redundancy was conducted. The alteration of the indicators before and after hydropower and photovoltaic operation was then analyzed using the cumulative probability distribution curve. Additionally, a concentration index was introduced to analyze the variations in hydrological impacts during different months. The results show that the hydro–photovoltaic complementary operation changed the subdaily natural flow regime, significantly increasing the rate of flow increase or decrease and the duration, with most indexes increasing by more than 100% compared with the natural flow regime. Furthermore, the concentration values of the indexes for the hydro–photovoltaic complementary operation were less than 10, indicating a more significant impact on the subdaily flow regime throughout the year. This research provides crucial data for mitigating ecological impacts under multi-source complementary scheduling. Full article
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