scholarly journals Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

2017 ◽  
Vol 17 (12) ◽  
pp. 2301-2312 ◽  
Author(s):  
Robbie Jones ◽  
Vern Manville ◽  
Jeff Peakall ◽  
Melanie J. Froude ◽  
Henry M. Odbert
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Rainfall Intensity ◽  
Model Performance ◽  
Eruptive Activity ◽  
Wet Season ◽  
Time Prediction ◽  
Antecedent Conditions ◽  
Synoptic Weather ◽  
Rainfall Seasonality

Abstract. Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.

scholarly journals Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

2017 ◽  
Author(s):  
Robbie Jones ◽  
Vern Manville ◽  
Jeff Peakall ◽  
Melanie Froude ◽  
Henry Odbert
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Rainfall Intensity ◽  
Model Performance ◽  
Eruptive Activity ◽  
Wet Season ◽  
Time Prediction ◽  
Antecedent Conditions ◽  
Synoptic Weather ◽  
Rainfall Seasonality

Abstract. Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River Valley at Soufrière Hills Volcano, Montserrat collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak one-hour rainfall intensity throughout the two-year dataset, and is higher under given rainfall conditions in year one than year two. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.

scholarly journals Satellite-based modelling of potential tsetse (Glossina pallidipes) breeding and foraging sites using teneral and non-teneral fly occurrence data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Stella Gachoki ◽  
Thomas Groen ◽  
Anton Vrieling ◽  
Michael Okal ◽  
Andrew Skidmore ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Land Surface ◽  
Large Scale ◽  
Model Performance ◽  
Glossina Pallidipes ◽  
Tsetse Flies ◽  
Wet Season ◽  
Distribution Models ◽  
Breeding Sites ◽  
Occurrence Data

Abstract Background African trypanosomiasis, which is mainly transmitted by tsetse flies (Glossina spp.), is a threat to public health and a significant hindrance to animal production. Tools that can reduce tsetse densities and interrupt disease transmission exist, but their large-scale deployment is limited by high implementation costs. This is in part limited by the absence of knowledge of breeding sites and dispersal data, and tools that can predict these in the absence of ground-truthing. Methods In Kenya, tsetse collections were carried out in 261 randomized points within Shimba Hills National Reserve (SHNR) and villages up to 5 km from the reserve boundary between 2017 and 2019. Considering their limited dispersal rate, we used in situ observations of newly emerged flies that had not had a blood meal (teneral) as a proxy for active breeding locations. We fitted commonly used species distribution models linking teneral and non-teneral tsetse presence with satellite-derived vegetation cover type fractions, greenness, temperature, and soil texture and moisture indices separately for the wet and dry season. Model performance was assessed with area under curve (AUC) statistics, while the maximum sum of sensitivity and specificity was used to classify suitable breeding or foraging sites. Results Glossina pallidipes flies were caught in 47% of the 261 traps, with teneral flies accounting for 37% of these traps. Fitted models were more accurate for the teneral flies (AUC = 0.83) as compared to the non-teneral (AUC = 0.73). The probability of teneral fly occurrence increased with woodland fractions but decreased with cropland fractions. During the wet season, the likelihood of teneral flies occurring decreased as silt content increased. Adult tsetse flies were less likely to be trapped in areas with average land surface temperatures below 24 °C. The models predicted that 63% of the potential tsetse breeding area was within the SHNR, but also indicated potential breeding pockets outside the reserve. Conclusion Modelling tsetse occurrence data disaggregated by life stages with time series of satellite-derived variables enabled the spatial characterization of potential breeding and foraging sites for G. pallidipes. Our models provide insight into tsetse bionomics and aid in characterising tsetse infestations and thus prioritizing control areas. Graphical abstract

Rapid Prediction of Strong Ground Motions from Major Earthquakes: An Example in the Wudu Basin, Sichuan, China

2021 ◽  
Author(s):  
Xiaolong Zhang ◽  
Xiaobo Peng ◽  
Shaolin Chen ◽  
Xiaojun Li ◽  
Zhan Dou ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Real Time ◽  
Large Scale ◽  
Ground Motions ◽  
Prediction Method ◽  
Real Data ◽  
Time Prediction ◽  
Impulse Function ◽  
Rapid Prediction ◽  
Strong Ground

ABSTRACT Rapid prediction of ground motions of major earthquakes can assist emergency response and postseismic damage assessment. Numerical simulations can perform such prediction. However, they need a large number of calculations based on real data and depend on large-scale computation resources, limiting practice of real-time prediction. To solve this problem, this article proposes a new prediction method based on a 3D transfer function, which is obtained by solving the site seismic response to three impulse function excitations that can be calculated by different pre-event 3D physics-based numerical simulations. This approach only needs one seismic record in the study area and can quickly (near-real time) predict ground motions in the whole study area. We verify the proposed approach with an Mw 5.7 aftershock of the 2008 Mw 7.9 Wenchuan earthquake in the Wudu basin, Sichuan Province, China. The results show that this method is feasible and has sufficient accuracy in nearly real-time prediction of seismic ground motions.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Real-Time Prediction of Tropical Cyclone Intensity Using COAMPS-TC

2012 ◽  
Author(s):  
J. D. Doyle ◽  
R. M. Hodur ◽  
S. Chen ◽  
H. Jin ◽  
Y. Jin ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Time Prediction

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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