Online Information as Real-Time Big Data About Heavy Rain Disasters and its Limitations: Case Study of Miyagi Prefecture, Japan, During Typhoons 17 and 18 in 2015

The role of public online information in helping to reduce disaster damage is expected to become increasingly important since it can be used for decision making about disaster response. This paper aims to discuss the effectiveness and limitations of real-time online information about heavy rainfall based on an analysis of data on the disaster caused by Typhoons 17 and 18 in 2015 in Miyagi prefecture, Japan, and on a focus group interview survey with four experts on natural disasters. The results from the interviews showed the following: (1) Landslide alert information is reliable for prediction purposes. However, many people did not monitor it because it was released around midnight. (2) Areas of landslide occurrence and river flooding correspond to areas with heavy cumulative rainfall. Yet cumulative rainfall data are not available on the web. (3) The available radar-rainfall data can be used to predict the situation one hour from the present as long as the person has expert knowledge. (4) It is possible to monitor river water levels at many points. Yet, about half of the observation points have no established “flood danger water level.” (5) Local governments released a great amount of disaster information through social media before flooding occurred on some rivers. However, one must monitor multiple social media accounts and not just the account of one’s hometown.

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Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data

Journal of Hydroinformatics ◽

10.2166/hydro.2013.161 ◽

2013 ◽

Vol 15 (3) ◽

pp. 897-912 ◽

Cited By ~ 15

Author(s):

S. Thorndahl ◽

M. R. Rasmussen

Keyword(s):

Real Time ◽

Water Levels ◽

Rainfall Data ◽

Storm Water ◽

Water Runoff ◽

Short Term ◽

Radar Rainfall ◽

Storm Water Runoff ◽

Short Term Forecasting ◽

Urban Storm

Model-based short-term forecasting of urban storm water runoff can be applied in real-time control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly. The radar rainfall extrapolation (nowcast) limits the lead time of the system to 2 hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented.

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Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Hydrological Sciences Journal ◽

10.1623/hysj.49.6.945.55721 ◽

2004 ◽

Vol 49 (6) ◽

Cited By ~ 4

Author(s):

Li Zhijia ◽

Ge Wenzhong ◽

Liu Jintao ◽

Zhao Kun

Keyword(s):

Real Time ◽

Hydrological Model ◽

Weather Radar ◽

Flood Forecasting ◽

Rainfall Data ◽

Distributed Hydrological Model ◽

Temps Réel ◽

Radar Rainfall ◽

Temps Reel ◽

Couplage De Données

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Enabling Rapid Classification of Social Media Communications During Crises

International Journal of Information Systems for Crisis Response and Management ◽

10.4018/ijiscram.2016070101 ◽

2016 ◽

Vol 8 (3) ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Muhammad Imran ◽

Prasenjit Mitra ◽

Jaideep Srivastava

Keyword(s):

Machine Learning ◽

Social Media ◽

Real Time ◽

Poor Performance ◽

Crisis Response ◽

Classification Performance ◽

Sudden Onset ◽

Supervised Machine Learning ◽

Online Information ◽

Real Time Analysis

The use of social media platforms such as Twitter by affected people during crises is considered a vital source of information for crisis response. However, rapid crisis response requires real-time analysis of online information. When a disaster happens, among other data processing techniques, supervised machine learning can help classify online information in real-time. However, scarcity of labeled data causes poor performance in machine training. Often labeled data from past event is available. Can past labeled data be reused to train classifiers? We study the usefulness of labeled data of past events. We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. Moreover, we propose two approaches (target labeling and active learning) to boost classification performance of a learning scheme. We perform extensive experimentation on real crisis datasets and show the utility of past-labeled data to train machine learning classifiers to process sudden-onset crisis-related data in real-time.

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Determinants of online intellectual capital disclosure by Spanish local governments

Journal of Intellectual Capital ◽

10.1108/jic-03-2020-0086 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yolanda Ramírez ◽

Ángel Tejada ◽

María Pilar Sánchez

Keyword(s):

Social Media ◽

Local Government ◽

Intellectual Capital ◽

Local Governments ◽

Positive Influence ◽

Online Information ◽

Content Type ◽

Body Of Knowledge ◽

Regression Techniques ◽

High Level

PurposeThis paper aims to investigate the extent of intellectual capital disclosure (ICD) through websites and social media in Spanish local government (SLG) and analyze the factors that explain their disclosure.Design/methodology/approachThe study applies content analysis and regression techniques. The ICD is analyzed for Spanish municipalities with more than 100,000 inhabitants and provincial capitals over a period from January 2018 to February 2020.FindingsFindings emphasize that the quantity of disclosed information on intellectual capital (IC) is in the low level, particularly with regard to human capital (HC). Furthermore, the results show that the information provided via social media mainly concerns the relational capital (RC). On the other hand, results obtained indicate that larger municipalities, with lower financial autonomy and whose citizens have a high income level use the online media (both websites and social media) more actively to disclose information about IC. Finally, municipalities led by women and with high level of citizens' education exert a positive influence in the ICD only on websites.Practical implicationsThis paper makes a number of key contributions to the existing body of knowledge, focusing on ICD, a neglected area in the public sector accounting literature. It explores and identifies the supply-side and demand-side determinants of information affecting the ICD in local governments. The results of this research could be useful for policymakers, regulators and governments' managers to improve the online information addressing ICD issues.Originality/valueThis paper adopts an innovative perspective by investigating the use of alternative tools for ICD in local government context (websites and social media). To the best of the authors’ knowledge, this is the first study that focuses on investigating the determinants of online ICD in local governments.

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