Study on the Uncertainty of Critical Rainfall for Flash Floods in Small Watersheds Based on the Random Rainfall Pattern

2021 ◽  
Author(s):  
Wenlin Yuan ◽  
Lu Lu ◽  
Hanzhen Song ◽  
Xiang Zhang ◽  
Linjuan Xu ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Flash Floods ◽  
Rainfall Pattern ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Early Warning Model ◽  
Forecast Lead Time ◽  
Critical Rainfall ◽  
Warning Model

Abstract Flash floods cause great harm to people's lives and property safety. Rainfall is the key factor which induces flash floods, and critical rainfall (CR) is the most widely used indicator in flash flood early warning systems. Due to the randomness of rainfall, the CR has great uncertainty, which causes missed alarms when predicting flash floods. To improve the early warning accuracy for flash floods, a random rainfall pattern (RRP) generation method based on control parameters, including the comprehensive peak position coefficient (CPPC) and comprehensive peak ratio (CPR), is proposed and an early warning model with dynamic correction based on RRP identification is established. The rainfall-runoff process is simulated by the HEC-HMS hydrological model, and the CR threshold space corresponding to the RRP set is calculated based on the trial algorithm. Xinxian, a small watershed located in Henan Province, China, is taken as the case study. The results show that the method for generating the RRP is practical and simple, and it effectively reflects the CR uncertainty caused by the rainfall pattern uncertainty. The HEC-HMS model is proved to have good application performance in the Xinxian watershed. Through sensitivity analysis, the effect of the antecedent soil moisture condition, CPPC, and CPR are compared. The proposed early warning model is practical and effective, which increases the forecast lead time.

scholarly journals Study on the Early Warning and Forecasting of Flash Floods in Small Watersheds Based on the Rainfall Pattern of Risk Probability Combination

2021 ◽  
Author(s):  
Lu Lu ◽  
Wenlin Yuan ◽  
Chengguo Su ◽  
Qianyu Gao ◽  
Denghua Yan ◽  
...  
Keyword(s):  
Early Warning ◽  
Rainfall Intensity ◽  
Flash Floods ◽  
Rainfall Pattern ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Small Watersheds ◽  
Rainfall Process ◽  
Risk Probability ◽  
Critical Rainfall

Abstract Flash floods cause great harm to people's life and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. This paper proposes a novel rainfall pattern based on total rainfall and peak rainfall intensity, i.e., the rainfall pattern of risk probability combination (RPRPC). To determine the joint distribution function with the best fitting effect, copula functions are introduced and optimized. On this basis, the HEC-HMS hydrological model is used to simulate the rainfall-runoff process, a trial algorithm is used to calculate the critical rainfall (CR), and an optimistic-general-pessimistic (O-G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern (TRP) and that the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, the influence of antecedent soil moisture condition (ASMC) and rainfall pattern on critical rainfall varies with the change of peak rainfall intensity and rainfall duration. Finally, the O-G-P early warning mode is effective and provides a valuable reference for the early warning and forecasting of flash floods in small watersheds in hilly areas.

Development of early warning system for linear engineering - a case study on highway in Sichuan, China

2020 ◽  
Author(s):  
Ruihua Xiao
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Highway Safety ◽  
Warning Systems ◽  
Safety Control ◽  
Early Warning Model ◽  
Landslide Early Warning ◽  
Warning Model

<p>For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.</p>

scholarly journals Adopting the <i>I</i><sub>3</sub>–<i>R</i><sub>24</sub> rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides

2018 ◽  
Vol 18 (6) ◽  
pp. 1717-1733 ◽  
Author(s):  
Lun-Wei Wei ◽  
Chuen-Ming Huang ◽  
Hongey Chen ◽  
Chyi-Tyi Lee ◽  
Chun-Chi Chi ◽  
...  
Keyword(s):  
Early Warning ◽  
Landslide Susceptibility ◽  
Local Governments ◽  
Regional Scale ◽  
Early Warning Systems ◽  
Rainfall Thresholds ◽  
Essential Information ◽  
Early Warning Model ◽  
Rainfall Index ◽  
Warning Model

Abstract. Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3 h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24 h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3–R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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