Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of “landslide events”. This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events (“news”). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined.
Landslides are a worldwide natural hazard that cause more damage and casualties than other hazards. Therefore, social and economic losses can be reduced through a landslide quantitative risk assessment (QRA). In the last two decades, many attempts of quantitative analysis on various scales have been performed; nevertheless, the major difficulty of QRA lies in how precise and reliable the assessment should have to be useful. For this reason, in this paper, we analyzed different freely available datasets and some products of previous research to assess the soundness of the outcomes performed by a recent QRA of slow-moving landslides in the Arno River basin (Central Italy). The validation process was carried out by comparing the abovementioned datasets and two components of the selected QRA (hazard and risk). The obtained results showed a robust correlation between most of the testing dataset and risk components, highlighting the accuracy of the selected QRA.
Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to “landslide news”. Analysis was applied to obtain information about temporal (2010–2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between “landslide news”, traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.
<p>Landslides and floods in Italy are the most frequent and diffuse natural hazards causing fatalities and damages to urban areas. Traditional methods as photo-interpretation, remote sensing or retrieval data from technical reports are the most common to set up event inventories. These systems rarely rely on automated or real-time updates. The retrieval of data, using specific data mining algorithms, from newspapers allows continuous feedback from real world and can further extend the exploitable data. Exploiting the data from mass media allows to get information about disaster situations with a relatively high temporal and spatial resolution to map natural hazards across various locations. Several techniques have been developed to mine data for different natural hazard, but rarely applied about landslide and flood news. The algorithm Semantic Engine to Classification and Geotagging News (SECaGN), based on a semantic engine, automatically retrieves information from online newspaper. 184.322 newspaper articles have been harvested from 2010 to 2019, referred to 32.525 landslide news and to 34.560 floods news in Italy. In this work, the data harvested by SECaGN underwent to a manual classification based on news relevance, localization accuracy and time of publication. Most of the news referred to recent events or are generically referred to landslide or floods (remediation work, hazard scenarios) and only a minimum part it was made up by wrong news. This classification allowed to identify the &#8220;true news&#8221; and to reject the data not appropriate, reducing the uncertainties.</p> <p>The harvested data have been used to identify the media impact of the events (both landslides or floods), their temporal distribution and those areas where more events happened, allowing a fast hazard estimation of the Country.</p> <p>The retrieved news data have been then compared with traditional sensors (e.g. rain gauges) and official reports about victims, damages, funds for soil protection and risk maps. Results did not show any clear correlation between the distribution of news and the other parameters, but it resulted that the regions that experienced a relevant number of events recorded lower funds for soil protection and vice versa.</p> <p>In conclusion, this work allowed to demonstrate the possibility of using automatically retrieved data from newspaper to create a reliable landslide (and flood) inventory, to be used as a proxy for hazard assessment over wide areas and to investigate the distribution of the phenomena and their correlation with other parameters, providing a powerful tool for a rapid hazard assessment in support of public authorities and decision makers.</p>
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