scholarly journals A Bayesian approach to exploring the influence of climate variability modes on fire weather conditions and lightning-ignited wildfires

2021 ◽  
Author(s):  
Bryson C. Bates ◽  
Andrew J. Dowdy ◽  
Lachlan McCaw
Keyword(s):  
Climate Variability ◽  
Network Architecture ◽  
Large Scale ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Fire Weather ◽  
Prescribed Burn ◽  
Data Set ◽  
Climate Modes

AbstractUnderstanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.

scholarly journals Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Siliang Luan ◽  
Qingfang Yang ◽  
Wei Wang ◽  
Zhongtai Jiang ◽  
Ruru Xing ◽  
...  
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Time Cost ◽  
Frequency Method ◽  
Regret Minimization ◽  
Accident Probability ◽  
Black Spots ◽  
Random Regret Minimization

The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

From Entrepreneur to Big Player

2013 ◽  
Vol 4 (2) ◽  
pp. 21-34
Author(s):  
Ruey-Shiang Shaw ◽  
Sheng-Pao Shih ◽  
Ta-Yu Fu ◽  
Chia-Wen Tsai
Keyword(s):  
Business Model ◽  
Large Scale ◽  
Software Industry ◽  
Stable Phase ◽  
Data Set ◽  
Main Research ◽  
Software Products ◽  
Ex Post ◽  
Major Player

The software industry faces drastic changes in technology and business operations. The research structure of this study is based on the business model for software industries proposed by Rajala in 2003. The researcher employed an ex post facto research design to conduct a case study of the Galaxy Software Service Co., a company that is representative of the software industry in Taiwan. The main research goal of this study is to explore how this particular company developed into a large software company in the Taiwanese software sector, which is characterized by a prevalence of small- and medium-sized businesses, over a period of 25 years. This study employs a case study design and relies on in-depth participation and interviews to acquire a complete data set of the company’s internal operations. The evolution of the business model from the company’s inception until the present day has been divided into four phases: the entrepreneur phase, the growth phase, the stable phase, and the innovative breakthrough phase. The company developed into a major player in the software industry for 3 reasons: it has always insisted on a product differentiation strategy based on the sole reliance on software products, it started out as a software products dealer and gradually developed its own research and development capability, and it built a large-scale project management capability and received CMMI certification. These factors make the company stand out from other System Integrated businesses in the Taiwanese software sector offering both hardware and software products.

Exploration of Tourist Activities in Urban Destination Using Venue Check-In Data

2019 ◽  
Vol 44 (3) ◽  
pp. 472-498
Author(s):  
Huy Quan Vu ◽  
Jian Ming Luo ◽  
Gang Li ◽  
Rob Law
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Traditional Approach ◽  
Urban Tourism ◽  
Data Set ◽  
Tourism Marketing ◽  
Large Scale Data ◽  
New Type ◽  
Scale Data

Understanding the differences and similarities in the activities of tourists from various cultures is important for tourism managers to develop appropriate plans and strategies that could support urban tourism marketing and managements. However, tourism managers still face challenges in obtaining such understanding because the traditional approach of data collection, which relies on survey and questionnaires, is incapable of capturing tourist activities at a large scale. In this article, we present a method for the study of tourist activities based on a new type of data, venue check-ins. The effectiveness of the presented approach is demonstrated through a case study of a major tourism country, France. Analysis based on a large-scale data set from 19 tourism cities in France reveals interesting differences and similarities in the activities of tourists from 14 markets (countries). Valuable insights are provided for various urban tourism applications.

scholarly journals Health Check-up of Commercial Banks in the Framework of CAMEL: A Case Study of Joint Venture Banks in Nepal

2007 ◽  
Vol 2 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Keshar J. Baral
Keyword(s):  
Commercial Banks ◽  
Large Scale ◽  
Joint Venture ◽  
Balance Sheet ◽  
Health Check ◽  
Annual Reports ◽  
Financial Health ◽  
Data Set ◽  
Business Studies

Using the data set published by joint venture banks in their annual reports, and NRB in its supervision annual reports, this paper examines the financial health of joint venture banks in the CAMEL framework. The health check up conducted on the basis of publicly available financial data concludes that the health of joint venture banks is better than that of the other commercial banks. In addition, the perusal of indicators of different components of CAMEL indicates that the financial health of joint venture banks is not so strong to manage the possible large scale shocks to their balance sheet and their health is fair. Journal of Nepalese Business Studies Vol.2(1) 2005 pp.41-55

scholarly journals Memristor Based Binary Convolutional Neural Network Architecture With Configurable Neurons

2021 ◽  
Vol 15 ◽  
Author(s):  
Lixing Huang ◽  
Jietao Diao ◽  
Hongshan Nie ◽  
Wei Wang ◽  
Zhiwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Precision ◽  
Network Architecture ◽  
Large Scale ◽  
Network Performance ◽  
Recognition Accuracy ◽  
Activation Function ◽  
Data Set ◽  
Neuron Activation

The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition capability. Consequently, it is very suitable for building a wearable embedded application system and has broad application prospects in image classification, speech recognition, and other fields. However, limited by the manufacturing process of memristive devices, high-precision weight devices are currently difficult to be applied in large-scale. In the same time, high-precision neuron activation function also further increases the complexity of network hardware implementation. In response to this, this paper proposes a configurable full-binary convolutional neural network (CFB-CNN) architecture, whose inputs, weights, and neurons are all binary values. The neurons are proportionally configured to two modes for different non-ideal situations. The architecture performance is verified based on the MNIST data set, and the influence of device yield and resistance fluctuations under different neuron configurations on network performance is also analyzed. The results show that the recognition accuracy of the 2-layer network is about 98.2%. When the yield rate is about 64% and the hidden neuron mode is configured as −1 and +1, namely ±1 MD, the CFB-CNN architecture achieves about 91.28% recognition accuracy. Whereas the resistance variation is about 26% and the hidden neuron mode configuration is 0 and 1, namely 01 MD, the CFB-CNN architecture gains about 93.43% recognition accuracy. Furthermore, memristors have been demonstrated as one of the most promising devices in neuromorphic computing for its synaptic plasticity. Therefore, the CFB-CNN architecture based on memristor is SNN-compatible, which is verified using the number of pulses to encode pixel values in this paper.

scholarly journals An attempt to deal with flash floods using a probabilistic hydrological nowcasting chain: a case study

2013 ◽  
Vol 1 (6) ◽  
pp. 7497-7515 ◽  
Author(s):  
F. Silvestro ◽  
N. Rebora ◽  
G. Cummings
Keyword(s):  
Large Scale ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Flash Floods ◽  
Medium Size ◽  
Small Areas ◽  
Anticipation Time ◽  
Prediction Systems ◽  
Meteorological Models

Abstract. The forecast of flash floods is sometimes impossible. In the last two decades, Numerical Weather Prediction Systems have become increasingly reliable with very relevant improvements in terms of quantitative precipitation forecasts. However, some types of events, those that are intense and localized in small areas, are still very difficult to predict. In many cases meteorological models fail to predict the volume of precipitable water at the large scale. Despite the application of modern probabilistic chains that uses precipitation downscaling algorithms in order to forecast the streamflow, some significant flood events remain unpredicted. This was also the case with an event which occurred on 8 and 9 June 2011 in the eastern part of the Liguria Region, Italy. This event affected in particular the Entella basin, which is quite a small watershed that flows into the Mediterranean Sea. The application of a hydrological nowcasting chain as a tool for predicting flash-floods in small and medium size basins with an anticipation time of a few hours (2–5) is here presented. This work investigated the "behaviour" of the chain in the cited event and how it could be exploited for operational purposes. The results in this particular case were encouraging.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

scholarly journals Tourist Activity Analysis by Leveraging Mobile Social Media Data

2017 ◽  
Vol 57 (7) ◽  
pp. 883-898 ◽  
Author(s):  
Huy Quan Vu ◽  
Gang Li ◽  
Rob Law ◽  
Yanchun Zhang
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Contextual Information ◽  
Online Social Network ◽  
Data Set ◽  
Travel Diary ◽  
Manual Recording ◽  
Rich Information ◽  
Media Data

Approaches to traditional travel diary construction rely on tourist participation and manual recording; hence, they are not only time-consuming but also limited in the scale and the number of samples. Online social network platforms have been used as alternative data sources for capturing the movements and travel patterns of tourists at a large scale. However, they fail to provide detailed contextual information on tourist activities for further analysis. In this paper, we present a new approach to travel diary construction based on the venue check-in data available in mobile social media with rich information on locations, time, and activities. Our case study focuses on the inbound tourism in Hong Kong using a data set composed of 17,355 check-ins generated by 600 tourists. We demonstrate how the proposed travel diary can provide useful practical implications for applications in location management, transportation management, impact management, and tourist experience promotion among others.

scholarly journals Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

2014 ◽  
Vol 53 (5) ◽  
pp. 1193-1212 ◽  
Author(s):  
Taesam Lee ◽  
Changsam Jeong
Keyword(s):  
Parameter Estimation ◽  
Climate Variability ◽  
South Korea ◽  
Simulation Study ◽  
Extreme Events ◽  
Large Scale ◽  
Southern Oscillation ◽  
Estimation Method ◽  
Parameter Estimation Method

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Niño–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

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