Understanding the complex dynamics of global landslides is essential for disaster planners to make timely and effective decisions that save lives and reduce the economic impacts on society. Using NASA's inventory of global landslide data, we developed a new machine learning (ML)-based system for town planners, disaster recovery strategists, and landslide researchers. Our system revealed hidden knowledge about a range of complex scenarios created from five landslide feature attributes. Users of our system can select from a list of 1.295 10 64 possible global landslide scenarios to discover valuable knowledge and predictions about the selected scenario in an interactive manner. Three ML algorithms-anomaly detection, decomposition analysis, and automated regression analysis-are used to elicit detailed knowledge about 25 scenarios selected from 14,532 global landslide records covering 12,220 injuries and 63,573 fatalities across 157 countries. Anomaly detection, logistic regression, and decomposition analysis performed well for all scenarios under study, with the area under the curve averaging 0.951, 0.911, and 0.896, respectively. Moreover, the prediction accuracy of linear regression had a mean absolute percentage error of 0.255. To the best of our knowledge, our scenario-based ML knowledge discovery system is the first of its kind to provide a comprehensive understanding of global landslide data.
The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis. We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022. The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021. During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities. In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively. As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition. According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new Tornado catalogue that include present and past records of Tornados across Bangladesh maximizing use of available sources. Within this new Tornado database, 119 records were captured starting from 1838 till 2020 causing 8735 deaths and 97,868 injuries leaving more than 102,776 people affected in total. Moreover, using this new Tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of Tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the Tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.
Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countries, and all possible major social media sites. The study revealed 11,668 troubled locations classified successfully with outstanding precision, recall, and F1-score, all evaluated in ubiquitous environment covering mobile, tablet, desktop, and cloud platforms. The system generated interesting global threat maps for robust scenario set of $$3.71 \times {10}^{29}$$ 3.71 × 10 29 , to be reported as original fully autonomous remote sensing application of this kind. The research discloses attractive news and global threat-maps with trusted overall classification accuracy.
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