COASTAL EFFECTS OF TSUNAMI

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years...

Еще до того, как были опубликованы первая часть и части, в Интернете было несколько упоминаний о том, что египтянам были известны окаменелые деревья и пустынное стекло. Последний факт был проиллюстрирован фотографиями украшений Тутанхамона. На основании таких фактов можно было отрицать скорость окаменения древесины. Хотя это могло быть только предположением. Однако это также поставило бы под сомнение сделанные выводы.
Целевой поиск дал результаты, которые исключили такую возможность. В этой части полностью приведены заключительные положения Части 2, включая фотографии.

Chabahar Bay, in southeastern Iran, lies at the north of the Gulf of Oman and close to the Makran Subduction Zone, which makes it a region that is susceptible to tsunamis. This bay has an increasingly important role in Iran’s international trade, and therefore the assessment of the regional vulnerability to the effects of a tsunami is vital. Based on both the details of historical events and the results of numerical modeling of the propagation pattern of a tsunami in this region, this study assessed the vulnerability of buildings within the Chabahar Bay region to a tsunami event. The Papathoma Tsunami Vulnerability Assessment (PTVA) model was used to calculate a relative vulnerability index (RVI) for the affected buildings based on their physical and structural characteristics. The results showed that in a postulated worst-case-scenario tsunami event in the Chabahar Bay area, approximately 60 % of the residential buildings would be affected, a level of damage that is categorized as ‘‘Average’’ in the RVI classification. Overall, the economic losses related to the  damage of residential buildings due to a tsunami in the Chabahar Bay
area are anticipated to be the equivalent of US$ 16.5 million.

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquakerelated studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: "Felt." The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III-VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI.

(In Bahasa) Studi ini melakukan perbandingan pada 2 katalog global-daring kejadian tsunami di Indonesia (7 0 LU-12 0 LS; 90 0-141 0 BT) tahun 1608-2012. Situs tersebut yakni National Geophysical Data Centre NOAA USA dengan Novosibirsk Tsunami Laboratory (NTL) Rusia. Kami melakukan perbandingan secara kualitatif melalui penilaian data yang diobservasi dan secara kuantitatif melalui penjumlahan data yang tercatat. Perbandingan menunjukkan secara keseluruhan katalog NOAA lebih lengkap dikarenakan secara kuantitatif jumlah data tsunami Indonesia pada NOAA 24 data lebih banyak dibandingkan NTL, kemudian secara kualitatif katalog NOAA dilengkapi dengan data dan informasi efek tsunami. Katalog NTL memiliki keistimewaan dalam mencantumkan pusat gempabumi dengan lebih spesifik hingga wilayah kota, sedangkan katalog NOAA hanya sampai pada wilayah pulau/laut. Di sisi lain, kedua katalog fokus pada kebutuhan data yang berbeda yakni NOAA cenderung mencatat data pasca tsunami dan NTL mencatat data sumber pembangkit tsunami.

Kata kunci: tsunami, katalog, dan daring.

(In English) This study conducting comparing 2 online-global tsunami catalog of tsunami events in Indonesia (7 0 N-12 0 S; 90 0-141 0 E) at 1608-2012. They are National Geophysical Data Centre NOAA USA and Novosibirsk Tsunami Laboratory (NTL) Russia website. We did qualitative assessment by type of data observed and quantitative assessment by number of data record. Study shows, as quantitative, NOAA catalog is more complete due to number of data is 24 more than NTL, as qualitative, NOAA completed with data and information of tsunami effects. NTL catalogue has a feature on listed earthquake epicenter more specific to the cities, meanwhile NOAA catalog only until sea/island. On the other hand, both catalogue focused on different data needed, that is NOAA tend to write out post-tsunami and NTL tend to write out tsunami source parameter.

Keywords: tsunami, catalogue, online

An earthquake hit the coasts of Turkey and Greece on the 30th October 2020. The earthquake caused a mini-tsunami. Using tex-data collected through the LastQuake app, we undertake a sentiment analysis (SA) analysis according to the intensities reported by their users in the MMI scale. We concluded that the negative polarity in text data is an indicator of significant intensity in the MMI scale. Additional tests are needed to define a correlation with a specific intensity.

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. Social media posts from the 10th anniversary of the earthquakes, in L’Aquila, Haiti, Chile, and Canterbury constitute a valuable source of data to mine insights into the progress of their recovery. The sentiments of affected people have the potential to highlight the successes and gaps in the post-earthquake recovery process. We propose a new method to assess post-disaster recovery through sentiment analysis of disaster-related tweets from citizens. The procedure for assessing recovery consists so far of 9 phases namely, hashtag identification, hashtag selection, data collection, data extraction, data processing, classification, feature extraction, features reprocessing and features selection. The data collection starts with social media monitoring, we identify the hashtags related to the memorial of each specific earthquake and then we collect tweets using two sources:1) manually and 2) a third party vendor from which we can collect the tweets related to the selected hashtags seven days around the anniversary of the event. The data process prepares the data to be understood by a machine learning algorithm. The classification phase combines a machine learning algorithm and supervised classification, tweets are categorized as positive, negative and neutral. Based on this classification and the general content of the tweets it is then possible to assess the relative success of the recovery based on the satisfaction of the community with the process. To detect the sentiment of tweets, we have combined supervised classification in the case of L’Aquila and linguistic features in the case of Haiti to classify the text according to the tweeter’s assessment of the recovery process. In the case of L’Aquila, whose 10 year anniversary took place on the 6th April 2019, we have obtained 4,349 original tweets between the 5th and the 10th of April 2019 with the hashtag #L'Aquila. In the case of Haiti, the 10 years anniversary took place on the 12th January 2020, we have obtained 8,157 original tweets between the 7th and the 15th of January 2020. So far we have identified more than 40 tweets regarding the anniversary of the earthquake in Chile and two tweets with respect to the anniversary of the Canterbury earthquake. The preliminary sentiment analysis of the tweets for the cases of L’Aquila and Haiti evidences that the negative polarity prevails. We do not yet have a sample big enough to determine the polarity of the twitter posts in the case of Chile and Canterbury. Nevertheless, preliminary results allowed us to demonstrate that sentiment analysis is a feasible tool to evaluate the success of a post-disaster recovery process. Affected communities in L’Aquila and Haiti are not satisfied with the progress of the post-earthquake recovery. In the case of L’Aquila, reconstruction of private buildings is more advanced than public ones and that there has been a delay in the re-opening of schools and other urban facilities in the historical city centre. The complaints about the mismanagement of financial resources and corruption are common in both cases: L’Aquila and Haiti.