Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective – A case study of Hurricane Harvey

Social media has been widely used for emergency communication both in disaster-affected areas and unaffected areas. Comparing emotional reaction and information propagation between on-site users and off-site users from a spatiotemporal perspective can help better comprehend collective human behavior during natural disasters. In this study, we investigate sentiment and retweet patterns of disaster-affected areas and disaster-unaffected areas at different stages of Hurricane Harvey. The results show that off-site tweets were more negative than on-site tweets, especially during the disaster. As for retweet patterns, indifferent-neutral and positive tweets spread broader than mixed-neutral and negative tweets. However, negative tweets spread faster than positive tweets, which reveals that social media users were more sensitive to negative information in disaster situations. With the development of the disaster, social media users were more sensitive to on-site positive messages than off-site negative posts. This data-driven study reveals the significant effect of sentiment expression on the publication and re-distribution of disaster-related messages. It generates implications for emergency communication and disaster management.     

复频电导技术在隧洞超前探水中的应用

围岩富水带是隧道超前预报最关注的问题，目前，多使用电磁方法来预报围岩的含水性，但受到场地条件限制，面临掌子面条件、金属机具干扰、三维波场定位等诸多困难，特别是掌子面前方100 m范围内围岩的含水性预报，在国内外都是一个新的高难课题。在研究了岩体电导率与电容率复频特性的基础上，结合巴基斯坦NJ-TBM引水隧洞工程，开发了复频电导探水(CFC)技术。该技术基于电磁波反射与相干原理，选用100 kHz～10 MHz频率范围，采用电偶极子发射与阵列接收方式和偏移成像技术，突破了隧道内场地条件的限制。依据1/4相干波长确定含水体的位置，相干能量确定含水量的大小。在巴基斯坦NJ-TBM隧洞超前探水的应用，证明了该方法的可行性与有效性。      

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

Exploring the role of the sharing economy in disasters management

Improvements in information and communications technologies (ICTs) have facilitated the inclusion of the sharing economy (SE) in societies more than ever. In the aftermath of recent disasters, the SE played significant roles to help the affected people and support official responders. However, the literature has not effectively explored these roles, and thus, no framework can support the systematic inclusion of the SE in disasters management. This paper aims to address the gap through a two-stage exploratory research approach. First, we conduct a systematic literature review to identify the extent to which the SE is taken into consideration in disasters. After that, we investigate the role of three Iranian SE-based companies that were involved in the 2019 Iran floods response. We collect empirical data by conducting semi-structured interviews and reviewing official reports.Our findings indicate that very few studies discuss the different roles of the SE in disasters, although SE companies have often provided effective solutions to address critical post-disaster logistics challenges. Four research propositions are presented to describe emerging roles for SE companies. The contribution of our study is twofold. First, our research identifies the different roles that the SE could play in disasters and therefore, brings a new perspective to the literature. Second, the study suggests opportunities for collaboration and partnership models from the point of origin to delivery that can support coordination and logistics in disasters.     

基于ECMWF和WRF的嘉陵江流域面雨量预报精度评估

将降雨数值预报产品运用到水文预报中已经逐渐成为提高洪水作业预报的预见期的重要手段。为充分了解ECMWF(European Centre for Medium Range Weather Forecasts)和WRF(Weather Research and Forecasting model)2种数值天气预报产品对嘉陵江研究区面雨量预报的预报精度和误差分布,且为增强洪水预报精度的稳健性提供科学支持,采用TS评分、空报率、漏报率、正确率等指标,对嘉陵江地区7个气象分区内的2016年汛期面雨量预报结果进行了检验,分析了不同分区内各检验指标与预报时效的关系。结果表明:ECMWF数值预报产品和WRF数值预报产品均可用于该地区晴雨预报,且2种产品的预报精度随降水等级的增大呈增大趋势,随预报时效的增加呈减小趋势。综合而言,ECMWF数值预报产品对嘉陵江研究区的预报效果更好。     

基于二次曲面模型的煤层气可采量预测方法

目前煤层气的可采量预测方法较为粗略,基本不考虑原始煤体的瓦斯赋存规律,也不考虑抽采影响范围内残存瓦斯含量的分布,因此可采量预测误差较大。建立加权二次曲面数学模型来描述原始瓦斯赋存规律,以及抽采单元的残存瓦斯含量的分布,从而对地质单元内煤层气可采量进行精确计算与预测,为煤层气抽采工程提供设计依据,为矿井瓦斯防治提供基础数据。