A Storm-Triggered Landslide Monitoring and Prediction System: Formulation and Case Study

2010 ◽  
Vol 14 (12) ◽  
pp. 1-24 ◽  
Author(s):  
Diandong Ren ◽  
Lance M. Leslie ◽  
Rong Fu ◽  
Robert E. Dickinson ◽  
Xiang Xin

Abstract Predicting the location and timing of mudslides with adequate lead time is a scientifically challenging problem that is critical for mitigating landslide impacts. Here, a new dynamic modeling system is described for monitoring and predicting storm-triggered landslides and their ecosystem implications. The model ingests both conventional and remotely sensed topographic and geologic data, whereas outputs include diagnostics required for the assessment of the physical and societal impacts of landslides. The system first was evaluated successfully in a series of experiments under idealized conditions. In the main study, under real conditions, the system was assessed over a mountainous region of China, the Yangjiashan Creeping (YC) slope. For this data-rich section of the Changjiang River, the model estimated creeping rates that had RMS errors of ∼0.5 mm yr−1 when compared with a dataset generated from borehole measurements. A prediction of the creeping curve for 2010 was made that suggested significant slope movement will occur in the next 5 years, without any change in the current precipitation morphology. However, sliding will become imminent if a storm occurs in that 5-yr period that produces over 150 mm of precipitation. A sensitivity experiment shows that the identified location fails first, triggering domino-effect slides that progress upslope. This system for predicting storm-triggered landslides is intended to improve upon present warning lead times to minimize the impacts of shallow, fast moving, and therefore hazardous landslides.

2011 ◽  
Vol 8 (4) ◽  
pp. 6639-6681 ◽  
Author(s):  
J. S. Verkade ◽  
M. G. F. Werner

Abstract. Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.


2018 ◽  
Vol 50 (2) ◽  
pp. 709-724 ◽  
Author(s):  
Pan Liu ◽  
Xiaojing Zhang ◽  
Yan Zhao ◽  
Chao Deng ◽  
Zejun Li ◽  
...  

Abstract Accurate and reliable flood forecasting plays an important role in flood control, reservoir operation, and water resources management. Conventional hydrological parameter calibration is based on an objective function without consideration for forecast performance during lead-time periods. A novel objective function, i.e., minimizing the sum of the squared errors between forecasted and observed streamflow during multiple lead times, is proposed to calibrate hydrological parameters for improved forecasting. China's Baiyunshan Reservoir basin was selected as a case study, and the Xinanjiang model was used. The proposed method provided better results for peak flows, in terms of the value and occurrence time, than the conventional method. Specifically, the qualified rate of peak flow for 4-, 5-, and 6-h lead times in the proposed method were 69.2%, 53.8%, and 38.5% in calibration, and 60%, 40%, and 20% in validation, respectively. This compares favorably with the corresponding values for the conventional method, which were 53.8%, 15.4%, and 7.7% in calibration, and 20%, 20%, and 0% in validation, respectively. Uncertainty analysis revealed that the proposed method caused less parameter uncertainty than the conventional method. Therefore, the proposed method is effective in improving the performance during multiple lead times for flood mitigation.


2020 ◽  
Vol 9 (3) ◽  
pp. 387-401
Author(s):  
Abhishek P.G. ◽  
Maheshwar Pratap

This article applies value stream mapping (VSM) in a distribution warehouse after identifying and categorizing different warehousing wastes. The study suggests solutions for the reduction of each type of waste and employs lead time as the metric to understand the overall effectiveness of the suggested remedies. The distribution warehouse faced severe stock-out situations and high lead time for all deliveries. Current state and future state maps were utilized for mapping the current and revamped system, respectively. While existing studies on lean warehousing have utilized VSM to study a specific type of waste, this study extends it to include all types of warehousing waste, classifies them into seven types and provides a real case study along with evidence for improvement schemes for each category. This research, employing a case study, suggests an integrated lean warehousing method for design and operation of distribution warehouses. Dilemma/research question/purpose: Can the warehouse avoid stock-outs and decrease the lead time by identifying and reducing or eliminating the seven types of wastes in the warehouse operations? Theory: Lean management principle applied to a warehouse using value stream mapping to eliminate wastes. Type of the case: A problem-solving using lean tools carried out in a warehouse. Protagonist: Not needed. Options: Allow the current state to continue causing stock-outs and high lead times, or identify and reduce the wastes in the warehouse operations and avoid stock-outs, decrease lead times, and improve the overall efficiency of the warehouse. Discussions and case questions: Is it possible for the firm to go one step further than what the future state map has shown in the study? What other lean tools do you see fit to reduce or eliminate the types of waste outlined in this article?


2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.


2011 ◽  
Vol 3 (2) ◽  
pp. 128-140 ◽  
Author(s):  
S. Hoekstra ◽  
K. Klockow ◽  
R. Riley ◽  
J. Brotzge ◽  
H. Brooks ◽  
...  

Abstract Tornado warnings are currently issued an average of 13 min in advance of a tornado and are based on a warn-on-detection paradigm. However, computer model improvements may allow for a new warning paradigm, warn-on-forecast, to be established in the future. This would mean that tornado warnings could be issued one to two hours in advance, prior to storm initiation. In anticipation of the technological innovation, this study inquires whether the warn-on-forecast paradigm for tornado warnings may be preferred by the public (i.e., individuals and households). The authors sample is drawn from visitors to the National Weather Center in Norman, Oklahoma. During the summer and fall of 2009, surveys were distributed to 320 participants to assess their understanding and perception of weather risks and preferred tornado warning lead time. Responses were analyzed according to several different parameters including age, region of residency, educational level, number of children, and prior tornado experience. A majority of the respondents answered many of the weather risk questions correctly. They seemed to be familiar with tornado seasons; however, they were unaware of the relative number of fatalities caused by tornadoes and several additional weather phenomena each year in the United States. The preferred lead time was 34.3 min according to average survey responses. This suggests that while the general public may currently prefer a longer average lead time than the present system offers, the preference does not extend to the 1–2-h time frame theoretically offered by the warn-on-forecast system. When asked what they would do if given a 1-h lead time, respondents reported that taking shelter was a lesser priority than when given a 15-min lead time, and fleeing the area became a slightly more popular alternative. A majority of respondents also reported the situation would feel less life threatening if given a 1-h lead time. These results suggest that how the public responds to longer lead times may be complex and situationally dependent, and further study must be conducted to ascertain the users for whom the longer lead times would carry the most value. These results form the basis of an informative stated-preference approach to predicting public response to long (>1 h) warning lead times, using public understanding of the risks posed by severe weather events to contextualize lead-time demand.


2016 ◽  
Vol 834 ◽  
pp. 205-210
Author(s):  
Elena Loredana Terzea ◽  
Antonia Cristina Barascu ◽  
Iulian Razvan Soare

Batch processes includes paint manufacturing, food processing, pharmaceutical industry, etc. The paper focuses on the process of paint manufacturing. The main contribution is the design of the current value stream mapping, very useful to understand the causes of waste and lead-time. This paper points out the necessity of applying lean methods within automotive industry, sector of bumpers painting and assembly, based on a real case-study.


2008 ◽  
Vol 23 (2) ◽  
pp. 246-258 ◽  
Author(s):  
Kevin M. Simmons ◽  
Daniel Sutter

Abstract Conventional wisdom holds that improved tornado warnings will reduce tornado casualties, because longer lead times on warnings provide extra opportunities to alert residents who can then take precautions. The relationship between warnings and casualties is examined using a dataset of tornadoes in the contiguous United States between 1986 and 2002. Two questions are examined: Does a warning issued on a tornado reduce the resulting number of fatalities and injuries? Do longer lead times reduce casualties? It is found that warnings have had a significant and consistent effect on tornado injuries, with a reduction of over 40% at some lead time intervals. The results for fatalities are mixed. An increase in lead time up to about 15 min reduces fatalities, while lead times longer than 15 min increase fatalities compared with no warning. The fatality results beyond 15 min, however, depend on five killer tornadoes and consequently are not robust.


2021 ◽  
pp. 90-103
Author(s):  
A. RAHMOUNI ◽  
◽  
M. MEDDI ◽  
A. HAMOUDI SAAED ◽  
◽  
...  

An effective drought forecast is an important measure to mitigate some of its most damaging impacts. In this study we compare the effectiveness of two models: Markov Switching Model (MSM) and Robust Regression Model (RRM) with three different approaches to forecast hydrological drought events in the north-west of Algeria using Standardized Runoff Index (SRI). The validation of these models is carried out by hydro-climatic series of 41 stations for the period of 1968-2009. The values of SRI 3, SRI 6, and SRI 12 have been forecasted over lead times of 1 and 6 months. The performance of forecast results is measured using R2 and RMSE. For the lead time of 1 month, the results are quite similar for both models with slight superiority for the Markov chain process. The addition of the SPI or RDI indices as independent variables improves this performance for some stations while it decreases accuracy for other stations. However, forecast accuracy declines significantly as the lead time increases to 6 months particularly for regression results.


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