Flood control risk is one of the main risks affecting the safe operation of large-scale water transfer projects. Systematically identifying the flood control risk in the project and carrying out risk classification and hierarchical management are problems for project managers. Based on the theory of system and risk assessment, this paper starts with the various risk sources and risk events involved in the whole process of the flood disaster chain, the risk of flood disaster factors, the exposure of the disaster-bearing body, and the vulnerability of the disaster-originating environment are combined. Then, we systematically and comprehensively identify the flood control risks of a large-scale water transfer project, which are divided into four types of risk elements: rainfall–runoff; confluence and flow capacity; the geological characteristics of canal section; economic and social layouts. Specific risk factors are identified for each type of risk element, and a flood control risk evaluation index system for a water transfer project is proposed. According to the framework of the analytic hierarchy process (AHP), a quantitative assessment of comprehensive flood control for water transfer projects is carried out. Taking the middle route of the South-to-North Water Transfer Project in China as an example, this paper evaluates the integrated flood control risks of 39 engineering units, identifies six units with higher risk levels, analyzes the causes, and suggests engineering and non-engineering countermeasures to prevent and reduce the occurrence of risk accidents. This method is not only used for comprehensive flood control risk assessment and risk management in the operation and management stage of the large-scale inter-basin water transfer project, but also has a reference value in considering the optimal layout of the project water transmission line from the perspective of flood control in the planning and design stage.
For the purpose of improving the scientific nature, reliability, and accuracy of flood forecasting, it is an effective and practical way to construct a flood forecasting scheme and carry out real-time forecasting with consideration of different rain patterns. The technique for rain pattern classification is of great significance in the above-mentioned technical roadmap. With the rapid development of artificial intelligence technologies such as machine learning, it is possible and necessary to apply these new methods to assist rain classification applications. In this research, multiple machine learning methods were adopted to study the time-history distribution characteristics and conduct rain pattern classification from observed rainfall time series data. Firstly, the hourly rainfall data between 2003 and 2021 of 37 rain gauge stations in the Pi River Basin were collected to classify rain patterns based on the universally acknowledged dynamic time warping (DTW) algorithm, and the classifications were treated as the benchmark result. After that, four other machine learning methods, including the Decision Tree (DT), Long- and Short-Term Memory (LSTM) neural network, Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), were specifically selected to establish classification models and the model performances were compared. By adjusting the sampling size, the influence of different sizes on the classification was analyzed. Intercomparison results indicated that LightGBM achieved the highest accuracy and the fastest training speed, the accuracy and F1 score were 98.95% and 98.58%, respectively, and the loss function and accuracy converged quickly after only 20 iterations. LSTM and SVM have satisfactory accuracy but relatively low training efficiency, and DT has fast classification speed but relatively low accuracy. With the increase in the sampling size, classification results became stable and more accurate. Besides the higher accuracy, the training efficiency of the four methods was also improved.
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