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Statistical and Trend Analysis of Annual Maximum Daily Rainfall (AMDR) for Kuching City, Sarawak, Malaysia

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Abstract

Kuching city and its surrounding urban areas frequently experience extreme high annual maximum daily rainfall (AMDR), resulting in flash floods. This study aims to carry out statistical and trend analysis of extreme AMDR events for Kuching Airport rainfall station from 1975 to 2017. From the analysis, the AMDR records a high variability with a value of 36.9% while January has the highest occurrence of AMDR with 53.5% of the total data. Findings from the linear regression plot have shown that the AMDR has a slight decreasing trend over the past four decades though the trend was insignificant. Based on the drainage design capacity of Kuching city, AMDR of magnitude 180 mm was identified as a threshold. The frequency analysis results showed that the return period of flooding events with daily rainfall exceeding 180 mm was 2.69 years. The occurrence probability of the flood event at least once in 1, 2, 3, 4 and 5 years was 0.37, 0.60, 0.75, 0.84 and 0.90, respectively. This study contributed to understanding the magnitude and frequency of extreme high AMDR which could lead to flooding events in Kuching city.

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Plain Language Summary During the droughts of 2018 and 2019, Central Europe had a water deficit of about 112 and 145 Gt compared to an average year. As the water storage differences between winter and summer is about 150 Gt, the drought‐related deficit amounts to 73% and 94% of these annual variations. These mass variations can be observed with the twin satellite missions GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and its successor GRACE Follow‐On (launched May 2018). With the satellite observations, the change in the total water storage can be estimated, including ground water, soil water content, and surface waters such as lakes and rivers. During the 21st century, Central Europe experienced four major droughts in 2003, 2015, 2018, and 2019, and we document the severity of the more recent droughts with respect to earlier events. We also find no systematic offset between the GRACE and GRACE‐FO observations, so that the available satellite gravity record extends now over 18 years already.
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The objective of the chapter is to analyze Twitter data to extract sentiments and opinions in unstructured text. The team attempted to use contextual text analytics to categorize Twitter data to understand the positive or negative sentiments for COVID vaccinations and wish to highlight key concerns. Text clustering has been performed on positive and negative sentiments to understand the key themes behind them. We followed a two-step process. In the first step, we identified positive and negative sentiments from Twitter feeds. In the second step, we aggregated all sentiments into categories to deduce what the Twitterati is thinking about COVID-19 vaccinations.The whole analysis was performed using Python, including TextBlob and Vader libraries. TextBlob library uses the Naïve-Bayes (probabilistic algorithms using Bayes’s Theorem to predict the category of a text) classifier to assess the polarity of a sentence and generates a score ranging between −1 (strongly negative) and +1 (strongly positive). The Naïve Bayes classifier categorizes based on probabilities of events. Although it is a simple algorithm, it performs well in many text classification problems. On the other hand, the Vader library uses a lexical approach that uses preassigned scores labeled positive and negative for different words found in a text. These scores are based on pre-trained models classified as positive/negative by actual human beings.We then performed the topic extraction that discovers the keywords in sentiments that capture the recurring theme of a text and is widely used to analyze large sets of sentiments to identify the most common topics easily and efficiently. We found a large segment as neutral (53%) followed by a positive sentiment segment that contributed 36% of tweets. However, at the same time, many people (10%+) remain on the fence regarding the potential repercussions of COVID vaccines as they are relatively new and yet untested over longer periods of time. It is reasonable to expect that people are a bit skeptical about vaccinations. Text clustering of negative sentiments identified late vaccinations and side effects being the key concerns. Positive sentiments mainly were driven by the readiness of other vaccines and weak reactions following vaccinations. The study contributes to text mining literature by providing a framework for analyzing public sentiments. This can help to understand the key themes in negative sentiments related to COVID vaccinations and can help in adjusting policies.KeywordsSentiment analyticsTwitterCOVID-19Text clusteringTopic modelingOpinion extractionTopic extractionPost COVIDIntention mining
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Objective: The objective of this paper is to analyze approved areas of medical research related to COVID-19 from the United Arab Emirates (UAE) and World Health Organization (WHO) in order to identify key topics and themes for these two entities. The paper attempts to understand the key focus areas of the government and private agencies for further medical research in response to COVID-19. Research Design and Methods: In view of availability of large volumes of documents and advancements in computing systems, text mining has emerged as a significant tool to analyze large volumes of unstructured data. For this paper, we have applied latent semantic analysis (LSA) and singular value decomposition for text clustering. Findings: The findings of terms analysis results show various focus areas of medical research communities for UAE and WHO. Nutrition is a key theme of research in UAE whereas alternative medicines or infection study emerged as key focus areas for WHO. Further analysis of topic modeling indicates that topics like pneumonia and prevention approach has been a focus of approved research for WHO. Contribution/Value Added: The study contributes to text mining literature by providing a framework for analyzing research or policy documents at country or organization level. This can help to understand the key themes in COVID-19 response by various countries and organizations and identify the focus areas for them.KeywordsText miningCOVID-19Public policyInformation extractionTopic modelingText clustering
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